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Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Data Mining and Knowledge Discovery is intended to be the best technical publication in the field providing a resource collecting relevant common methods and techniques. Traditionally, data mining and knowledge discovery was performed manually. As time passed, the amount of data in many systems grew to larger than terabyte size, and could no longer be maintained manually. Besides, for the successful existence of any business, discovering underlying patterns in data is considered essential. This paper proposed about applications, techniques and trends of Data Mining and Knowledge Discovery Database. By Khin Sein Hlaing | Yin Myo Kay Khine Thaw "Applications, Techniques and Trends of Data Mining and Knowledge Discovery Database" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26733.pdf Paper URL https://www.ijtsrd.com/computer-science/data-miining/26733/applications-techniques-and-trends-of-data-mining-and-knowledge-discovery-database/khin-sein-hlaing

Data Mining and Knowledge Discovery is intended to be the best technical publication in the field providing a resource collecting relevant common methods and techniques. Traditionally, data mining and knowledge discovery was performed manually. As time passed, the amount of data in many systems grew to larger than terabyte size, and could no longer be maintained manually. Besides, for the successful existence of any business, discovering underlying patterns in data is considered essential. This paper proposed about applications, techniques and trends of Data Mining and Knowledge Discovery Database. By Khin Sein Hlaing | Yin Myo Kay Khine Thaw "Applications, Techniques and Trends of Data Mining and Knowledge Discovery Database" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26733.pdf Paper URL https://www.ijtsrd.com/computer-science/data-miining/26733/applications-techniques-and-trends-of-data-mining-and-knowledge-discovery-database/khin-sein-hlaing

Data Mining and Knowledge Discovery is intended to be the best technical publication in the field providing a resource collecting relevant common methods and techniques. Traditionally, data mining and knowledge discovery was performed manually. As time passed, the amount of data in many systems grew to larger than terabyte size, and could no longer be maintained manually. Besides, for the successful existence of any business, discovering underlying patterns in data is considered essential. This paper proposed about applications, techniques and trends of Data Mining and Knowledge Discovery Database. By Khin Sein Hlaing | Yin Myo Kay Khine Thaw "Applications, Techniques and Trends of Data Mining and Knowledge Discovery Database" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26733.pdf Paper URL https://www.ijtsrd.com/computer-science/data-miining/26733/applications-techniques-and-trends-of-data-mining-and-knowledge-discovery-database/khin-sein-hlaing

In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises--termed "deep knowledge tracing" or "DKT"--has demonstrated a stunning performance advantage over the mainstay of the field, "Bayesian knowledge tracing" or "BKT." In this article, we attempt to understand the basis for DKT's advantage by considering the sources of statistical regularity in the data that DKT can leverage but which BKT cannot. We hypothesize four forms of regularity that BKT fails to exploit: recency effects, the contextualized trial sequence, inter-skill similarity, and individual variation in ability. We demonstrate that when BKT is extended to allow it more flexibility in modeling statistical regularities--using extensions previously proposed in the literature--BKT achieves a level of performance indistinguishable from that of DKT. We argue that while DKT is a powerful, useful, general-purpose framework for modeling student learning, its gains do not come from the discovery of novel representations--the fundamental advantage of deep learning. To answer the question posed in our title, knowledge tracing may be a domain that does "not" require 'depth'; shallow models like BKT can perform just as well and offer us greater interpretability and explanatory power. [For the full proceedings, see ED592609.]

In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises--termed "deep knowledge tracing" or "DKT"--has demonstrated a stunning performance advantage over the mainstay of the field, "Bayesian knowledge tracing" or "BKT." In this article, we attempt to understand the basis for DKT's advantage by considering the sources of statistical regularity in the data that DKT can leverage but which BKT cannot. We hypothesize four forms of regularity that BKT fails to exploit: recency effects, the contextualized trial sequence, inter-skill similarity, and individual variation in ability. We demonstrate that when BKT is extended to allow it more flexibility in modeling statistical regularities--using extensions previously proposed in the literature--BKT achieves a level of performance indistinguishable from that of DKT. We argue that while DKT is a powerful, useful, general-purpose framework for modeling student learning, its gains do not come from the discovery of novel representations--the fundamental advantage of deep learning. To answer the question posed in our title, knowledge tracing may be a domain that does "not" require 'depth'; shallow models like BKT can perform just as well and offer us greater interpretability and explanatory power. [For the full proceedings, see ED592609.]

In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises--termed "deep knowledge tracing" or "DKT"--has demonstrated a stunning performance advantage over the mainstay of the field, "Bayesian knowledge tracing" or "BKT." In this article, we attempt to understand the basis for DKT's advantage by considering the sources of statistical regularity in the data that DKT can leverage but which BKT cannot. We hypothesize four forms of regularity that BKT fails to exploit: recency effects, the contextualized trial sequence, inter-skill similarity, and individual variation in ability. We demonstrate that when BKT is extended to allow it more flexibility in modeling statistical regularities--using extensions previously proposed in the literature--BKT achieves a level of performance indistinguishable from that of DKT. We argue that while DKT is a powerful, useful, general-purpose framework for modeling student learning, its gains do not come from the discovery of novel representations--the fundamental advantage of deep learning. To answer the question posed in our title, knowledge tracing may be a domain that does "not" require 'depth'; shallow models like BKT can perform just as well and offer us greater interpretability and explanatory power. [For the full proceedings, see ED592609.]

In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises--termed "deep knowledge tracing" or "DKT"--has demonstrated a stunning performance advantage over the mainstay of the field, "Bayesian knowledge tracing" or "BKT." In this article, we attempt to understand the basis for DKT's advantage by considering the sources of statistical regularity in the data that DKT can leverage but which BKT cannot. We hypothesize four forms of regularity that BKT fails to exploit: recency effects, the contextualized trial sequence, inter-skill similarity, and individual variation in ability. We demonstrate that when BKT is extended to allow it more flexibility in modeling statistical regularities--using extensions previously proposed in the literature--BKT achieves a level of performance indistinguishable from that of DKT. We argue that while DKT is a powerful, useful, general-purpose framework for modeling student learning, its gains do not come from the discovery of novel representations--the fundamental advantage of deep learning. To answer the question posed in our title, knowledge tracing may be a domain that does "not" require 'depth'; shallow models like BKT can perform just as well and offer us greater interpretability and explanatory power. [For the full proceedings, see ED592609.]

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the areas of knowledge discovery and data mining (KDD). We had three keynote speeches, delivered by Sang Cha from Seoul National University, Rakesh Agrawal from Data Insights Laboratories, and Dacheng Tao from University of Sydney. In addition to the main technical program, the offerings of this conference were further enriched by three tutorials as well as four international workshops on leading-edge topics. We received a record-breaking number of 458 submissions from 36 countries all over the world. This highest number of submissions is very encouraging because it reflects the improving status of PAKDD. As a result, 129 out of 458 papers were accepted, yielding an acceptance rate of28.2 . Among them, 45 papers were selected as long-presentation papers, and 84 papers were selected as regular-presentation papers. Mining social networks or graph data was the most popular topic in the accepted papers. There are 288 participants from 28 countries all over the world.

Data mining (DM) is primarily used by businesses to discover customer tendencies to guarantee future profit opportunities. In the TAE&PD project we intend to incorporate a KDDM methodology using open source applications to gather, preprocess, model, evaluate and identify patterns of terrorism activity that may prove useful to counter-terrorism and strengthen homeland security in Afghanistan. We will experiment using real terrorism incidents data from the Worldwide Incidents Tracking System (WITS) of the National Counterterrorism Center (NCTC). The project seeks to discover terrorism trends based on specific incident factors, help in the evaluation of war in Afghanistan and demonstrate a KDDM approach that could be applied (proof of concept) to national security. Project results may uncover valuable information regarding terrorist hot spots to determine geographical mobilization of security forces resources in the region.

Cloud technology aims to replace the existing computing approach by giving access to both underlying hardware as well as application programs. Such services are made available through the world wide web. The cheap, flexible, as well as easy accessibility make it a desired choice. This provides limitless data storage and processing power, allowing that to mine massive amounts of information. Information mining techniques are used to locate data stored in databases. It is employed to analyze data gathered from different sources to extract useful details from the information. Data mining can also be used to identify patterns or values, categorize information, analyze information, as well as obtain patterns and associations in input data. It is required in many fields, including industry, scientific research, marketing, brand management, and healthcare. This paper discusses an integrative view of information retrieval as well as cloud technology to acquire easy accessibility to technology and creates a type of information retrieval network consisting of a significant number of decentralized data assessment solutions.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

2008 by IGI Global

2008 by IGI Global

2008 by IGI Global

2008 by IGI Global

Data mining is primarily used by businesses. Today companies with strong consumer focus depend on data mining to determine relationships among internal and external data factors that are being registered and stored digitally. It enables them to drill down data into summary information to view detail transactional data and reveal trends that could be beneficial for an organization s business decision making. In this paper we incorporate data mining techniques using open source applications to model, evaluate and identify patterns of terrorism activity in Afghanistan for counter-terrorism and to strengthen homeland security. We apply data mining techniques to real terrorism incidents data from the Worldwide Incidents Tracking System (WITS) of the National Counterterrorism Center (NCTC). The results of the study will help in the discovery of terrorist group tendencies based on specific incident factors, but will also help evaluate the war on terror in Afghanistan up to date. With these results we also look to uncover valuable information regarding terrorist hot spots to determine geographical mobilization of security forces resources in the region.

Data mining is primarily used by businesses. Today companies with strong consumer focus depend on data mining to determine relationships among internal and external data factors that are being registered and stored digitally. It enables them to drill down data into summary information to view detail transactional data and reveal trends that could be beneficial for an organization s business decision making. In this paper we incorporate data mining techniques using open source applications to model, evaluate and identify patterns of terrorism activity in Afghanistan for counter-terrorism and to strengthen homeland security. We apply data mining techniques to real terrorism incidents data from the Worldwide Incidents Tracking System (WITS) of the National Counterterrorism Center (NCTC). The results of the study will help in the discovery of terrorist group tendencies based on specific incident factors, but will also help evaluate the war on terror in Afghanistan up to date. With these results we also look to uncover valuable information regarding terrorist hot spots to determine geographical mobilization of security forces resources in the region.

In an effort to improve decision-making on the identity of unknown objects appearing in visual images when the surrounding environment may be noisy and cluttered, a highly sensitive target detection scheme is developed employing nonlinear dynamical equations. It is first shown that the signal to noise ratio of this particular operation on rudimentary signals can be amplified by a factor of over one million. This means (for elementary signals) that it is possible to effectively magnify the quality of information in an input signal. This procedure affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual rendering of a scene. Since image processing is an area in which the original data are stationary in some sense (auditory signals suffer from nonstationary effects), the algorithm is applied to a visual rendering scene in a noisy environment. A description of the mathematical details of the algorithm used for the image enhancement is described in the appendix for completeness. The algorithm is based on a concept from nonlinear dynamics, termed stochastic resonance. Such a procedure has a biological basis, and may be termed biomimicry or biologically inspired.

In an effort to improve decision-making on the identity of unknown objects appearing in visual images when the surrounding environment may be noisy and cluttered, a highly sensitive target detection scheme is developed employing nonlinear dynamical equations. It is first shown that the signal to noise ratio of this particular operation on rudimentary signals can be amplified by a factor of over one million. This means (for elementary signals) that it is possible to effectively magnify the quality of information in an input signal. This procedure affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual rendering of a scene. Since image processing is an area in which the original data are stationary in some sense (auditory signals suffer from nonstationary effects), the algorithm is applied to a visual rendering scene in a noisy environment. A description of the mathematical details of the algorithm used for the image enhancement is described in the appendix for completeness. The algorithm is based on a concept from nonlinear dynamics, termed stochastic resonance. Such a procedure has a biological basis, and may be termed biomimicry or biologically inspired.

In an effort to improve decision-making on the identity of unknown objects appearing in visual images when the surrounding environment may be noisy and cluttered, a highly sensitive target detection scheme is developed employing nonlinear dynamical equations. It is first shown that the signal to noise ratio of this particular operation on rudimentary signals can be amplified by a factor of over one million. This means (for elementary signals) that it is possible to effectively magnify the quality of information in an input signal. This procedure affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual rendering of a scene. Since image processing is an area in which the original data are stationary in some sense (auditory signals suffer from nonstationary effects), the algorithm is applied to a visual rendering scene in a noisy environment. A description of the mathematical details of the algorithm used for the image enhancement is described in the appendix for completeness. The algorithm is based on a concept from nonlinear dynamics, termed stochastic resonance. Such a procedure has a biological basis, and may be termed biomimicry or biologically inspired.

In an effort to improve decision-making on the identity of unknown objects appearing in visual images when the surrounding environment may be noisy and cluttered, a highly sensitive target detection scheme is developed employing nonlinear dynamical equations. It is first shown that the signal to noise ratio of this particular operation on rudimentary signals can be amplified by a factor of over one million. This means (for elementary signals) that it is possible to effectively magnify the quality of information in an input signal. This procedure affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual rendering of a scene. Since image processing is an area in which the original data are stationary in some sense (auditory signals suffer from nonstationary effects), the algorithm is applied to a visual rendering scene in a noisy environment. A description of the mathematical details of the algorithm used for the image enhancement is described in the appendix for completeness. The algorithm is based on a concept from nonlinear dynamics, termed stochastic resonance. Such a procedure has a biological basis, and may be termed biomimicry or biologically inspired.

In an effort to improve decision-making on the identity of unknown objects appearing in visual images when the surrounding environment may be noisy and cluttered, a highly sensitive target detection scheme is developed employing nonlinear dynamical equations. It is first shown that the signal to noise ratio of this particular operation on rudimentary signals can be amplified by a factor of over one million. This means (for elementary signals) that it is possible to effectively magnify the quality of information in an input signal. This procedure affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual rendering of a scene. Since image processing is an area in which the original data are stationary in some sense (auditory signals suffer from nonstationary effects), the algorithm is applied to a visual rendering scene in a noisy environment. A description of the mathematical details of the algorithm used for the image enhancement is described in the appendix for completeness. The algorithm is based on a concept from nonlinear dynamics, termed stochastic resonance. Such a procedure has a biological basis, and may be termed biomimicry or biologically inspired.

In an effort to improve decision-making on the identity of unknown objects appearing in visual images when the surrounding environment may be noisy and cluttered, a highly sensitive target detection scheme is developed employing nonlinear dynamical equations. It is first shown that the signal to noise ratio of this particular operation on rudimentary signals can be amplified by a factor of over one million. This means (for elementary signals) that it is possible to effectively magnify the quality of information in an input signal. This procedure affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual rendering of a scene. Since image processing is an area in which the original data are stationary in some sense (auditory signals suffer from nonstationary effects), the algorithm is applied to a visual rendering scene in a noisy environment. A description of the mathematical details of the algorithm used for the image enhancement is described in the appendix for completeness. The algorithm is based on a concept from nonlinear dynamics, termed stochastic resonance. Such a procedure has a biological basis, and may be termed biomimicry or biologically inspired.

In an effort to improve decision-making on the identity of unknown objects appearing in visual images when the surrounding environment may be noisy and cluttered, a highly sensitive target detection scheme is developed employing nonlinear dynamical equations. It is first shown that the signal to noise ratio of this particular operation on rudimentary signals can be amplified by a factor of over one million. This means (for elementary signals) that it is possible to effectively magnify the quality of information in an input signal. This procedure affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual rendering of a scene. Since image processing is an area in which the original data are stationary in some sense (auditory signals suffer from nonstationary effects), the algorithm is applied to a visual rendering scene in a noisy environment. A description of the mathematical details of the algorithm used for the image enhancement is described in the appendix for completeness. The algorithm is based on a concept from nonlinear dynamics, termed stochastic resonance. Such a procedure has a biological basis, and may be termed biomimicry or biologically inspired.

Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD 2000 Lyon, France, September 13–16, 2000 Proceedings Author: Djamel A. Zighed, Jan Komorowski, Jan Żytkow Published by Springer Berlin Heidelberg ISBN: 978-3-540-41066-9 DOI: 10.1007/3-540-45372-5 Table of Contents: Multi-relational Data Mining, Using UML for ILP An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data Basis of a Fuzzy Knowledge Discovery System Confirmation Rule Sets Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery Combining Multiple Models with Meta Decision Trees Materialized Data Mining Views Approximation of Frequency Queries by Means of Free-Sets Application of Reinforcement Learning to Electrical Power System Closed-Loop Emergency Control Efficient Score-Based Learning of Equivalence Classes of Bayesian Networks Quantifying the Resilience of Inductive Classification Algorithms Bagging and Boosting with Dynamic Integration of Classifiers Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information Some Enhancements of Decision Tree Bagging Relative Unsupervised Discretization for Association Rule Mining Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today’s Approaches Unified Algorithm for Undirected Discovery of Exception Rules Sampling Strategies for Targeting Rare Groups from a Bank Customer Database Instance-Based Classification by Emerging Patterns Context-Based Similarity Measures for Categorical Databases

Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD 2000 Lyon, France, September 13–16, 2000 Proceedings Author: Djamel A. Zighed, Jan Komorowski, Jan Żytkow Published by Springer Berlin Heidelberg ISBN: 978-3-540-41066-9 DOI: 10.1007/3-540-45372-5 Table of Contents: Multi-relational Data Mining, Using UML for ILP An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data Basis of a Fuzzy Knowledge Discovery System Confirmation Rule Sets Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery Combining Multiple Models with Meta Decision Trees Materialized Data Mining Views Approximation of Frequency Queries by Means of Free-Sets Application of Reinforcement Learning to Electrical Power System Closed-Loop Emergency Control Efficient Score-Based Learning of Equivalence Classes of Bayesian Networks Quantifying the Resilience of Inductive Classification Algorithms Bagging and Boosting with Dynamic Integration of Classifiers Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information Some Enhancements of Decision Tree Bagging Relative Unsupervised Discretization for Association Rule Mining Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today’s Approaches Unified Algorithm for Undirected Discovery of Exception Rules Sampling Strategies for Targeting Rare Groups from a Bank Customer Database Instance-Based Classification by Emerging Patterns Context-Based Similarity Measures for Categorical Databases

Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD 2000 Lyon, France, September 13–16, 2000 Proceedings Author: Djamel A. Zighed, Jan Komorowski, Jan Żytkow Published by Springer Berlin Heidelberg ISBN: 978-3-540-41066-9 DOI: 10.1007/3-540-45372-5 Table of Contents: Multi-relational Data Mining, Using UML for ILP An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data Basis of a Fuzzy Knowledge Discovery System Confirmation Rule Sets Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery Combining Multiple Models with Meta Decision Trees Materialized Data Mining Views Approximation of Frequency Queries by Means of Free-Sets Application of Reinforcement Learning to Electrical Power System Closed-Loop Emergency Control Efficient Score-Based Learning of Equivalence Classes of Bayesian Networks Quantifying the Resilience of Inductive Classification Algorithms Bagging and Boosting with Dynamic Integration of Classifiers Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information Some Enhancements of Decision Tree Bagging Relative Unsupervised Discretization for Association Rule Mining Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today’s Approaches Unified Algorithm for Undirected Discovery of Exception Rules Sampling Strategies for Targeting Rare Groups from a Bank Customer Database Instance-Based Classification by Emerging Patterns Context-Based Similarity Measures for Categorical Databases

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Graz university presentation course Knowledge Discovery and Data Mining 2 (VU) (707.004):  Deep learning