Applied data mining - Info and Reading Options

"Applied data mining" Table Of Contents:

• 1- Pt. I Fundamentals
• 2- 1.Introduction
• 3- 1.1.Background
• 4- 1.1.1.Data Mining-Definitions and Concepts
• 5- 1.1.2.Data Mining Process
• 6- 1.1.3.Data Mining Algorithms
• 7- 1.2.Organization of the Book
• 8- 1.2.1.Part 1: Fundamentals
• 9- 1.2.2.Part 2: Advanced Data Mining
• 10- 1.2.3.Part 3: Emerging Applications
• 11- 1.3.The Audience of the Book
• 12- 2.Mathematical Foundations
• 13- 2.1.Organization of Data
• 14- 2.1.1.Boolean Model
• 15- 2.1.2.Vector Space Model
• 16- 2.1.3.Graph Model
• 17- 2.1.4.Other Data Structures
• 18- 2.2.Data Distribution
• 19- 2.2.1.Univariate Distribution
• 20- 2.2.2.Multivariate Distribution
• 21- 2.3.Distance Measures
• 22- 2.3.1.Jaccard distance
• 23- 2.3.2.Euclidean Distance
• 24- 2.3.3.Minkowski Distance
• 25- 2.3.4.Chebyshev Distance
• 26- 2.3.5.Mahalanobis Distance
• 27- 2.4.Similarity Measures
• 28- 2.4.1.Cosine Similarity
• 29- 2.4.2.Adjusted Cosine Similarity
• 30- 2.4.3.Kullback-Leibler Divergence
• 31- 2.4.4.Model-based Measures
• 32- 2.5.Dimensionality Reduction
• 33- Contents note continued: 2.5.1.Principal Component Analysis
• 34- 2.5.2.Independent Component Analysis
• 35- 2.5.3.Non-negative Matrix Factorization
• 36- 2.5.4.Singular Value Decomposition
• 37- 2.6.Chapter Summary
• 38- 3.Data Preparation
• 39- 3.1.Attribute Selection
• 40- 3.1.1.Feature Selection
• 41- 3.1.2.Discretizing Numeric Attributes
• 42- 3.2.Data Cleaning and Integrity
• 43- 3.2.1.Missing Values
• 44- 3.2.2.Detecting Anomalies
• 45- 3.2.3.Applications
• 46- 3.3.Multiple Model Integration
• 47- 3.3.1.Data Federation
• 48- 3.3.2.Bagging and Boosting
• 49- 3.4.Chapter Summary
• 50- 4.Clustering Analysis
• 51- 4.1.Clustering Analysis
• 52- 4.2.Types of Data in Clustering Analysis
• 53- 4.2.1.Data Matrix
• 54- 4.2.2.The Proximity Matrix
• 55- 4.3.Traditional Clustering Algorithms
• 56- 4.3.1.Partitional methods
• 57- 4.3.2.Hierarchical Methods
• 58- 4.3.3.Density-based methods
• 59- 4.3.4.Grid-based Methods
• 60- 4.3.5.Model-based Methods
• 61- 4.4.High-dimensional clustering algorithm
• 62- 4.4.1.Bottom-up Approaches
• 63- 4.4.2.Top-down Approaches
• 64- Contents note continued: 4.4.3.Other Methods
• 65- 4.5.Constraint-based Clustering Algorithm
• 66- 4.5.1.COP K-means
• 67- 4.5.2.MPCK-means
• 68- 4.5.3.AFCC
• 69- 4.6.Consensus Clustering Algorithm
• 70- 4.6.1.Consensus Clustering Framework
• 71- 4.6.2.Some Consensus Clustering Methods
• 72- 4.7.Chapter Summary
• 73- 5.Classification
• 74- 5.1.Classification Definition and Related Issues
• 75- 5.2.Decision Tree and Classification
• 76- 5.2.1.Decision Tree
• 77- 5.2.2.Decision Tree Classification
• 78- 5.2.3.Hunt's Algorithm
• 79- 5.3.Bayesian Network and Classification
• 80- 5.3.1.Bayesian Network
• 81- 5.3.2.Backpropagation and Classification
• 82- 5.3.3.Association-based Classification
• 83- 5.3.4.Support Vector Machines and Classification
• 84- 5.4.Chapter Summary
• 85- 6.Frequent Pattern Mining
• 86- 6.1.Association Rule Mining
• 87- 6.1.1.Association Rule Mining Problem
• 88- 6.1.2.Basic Algorithms for Association Rule Mining
• 89- 6.2.Sequential Pattern Mining
• 90- 6.2.1.Sequential Pattern Mining Problem
• 91- Contents note continued: 6.2.2.Existing Sequential Pattern Mining Algorithms
• 92- 6.3.Frequent Subtree Mining
• 93- 6.3.1.Frequent Subtree Mining Problem
• 94- 6.3.2.Data Structures for Storing Trees
• 95- 6.3.3.Maximal and closed frequent subtrees
• 96- 6.4.Frequent Subgraph Mining
• 97- 6.4.1.Problem Definition
• 98- 6.4.2.Graph Representation
• 99- 6.4.3.Candidate Generation
• 100- 6.4.4.Frequent Subgraph Mining Algorithms
• 101- 6.5.Chapter Summary
• 102- pt. II Advanced Data Mining
• 103- 7.Advanced Clustering Analysis
• 104- 7.1.Introduction
• 105- 7.2.Space Smoothing Search Methods in Heuristic Clustering
• 106- 7.2.1.Smoothing Search Space and Smoothing Operator
• 107- 7.2.2.Clustering Algorithm based on Smoothed Search Space
• 108- 7.3.Using Approximate Backbone for Initializations in Clustering
• 109- 7.3.1.Definitions and Background of Approximate Backbone
• 110- 7.3.2.Heuristic Clustering Algorithm based on Approximate Backbone
• 111- 7.4.Improving Clustering Quality in High Dimensional Space
• 112- Contents note continued: 7.4.1.Overview of High Dimensional Clustering
• 113- 7.4.2.Motivation of our Method
• 114- 7.4.3.Significant Local Dense Area
• 115- 7.4.4.Projective Clustering based on SLDAs
• 116- 7.5.Chapter Summary
• 117- 8.Multi-Label Classification
• 118- 8.1.Introduction
• 119- 8.2.What is Multi-label Classification
• 120- 8.3.Problem Transformation
• 121- 8.3.1.Binary Relevance and Label Powerset
• 122- 8.3.2.Classifier Chains and Probabilistic Classifier Chains
• 123- 8.3.3.Decompose the Label Set
• 124- 8.3.4.Transform Original Label Space to Another Space
• 125- 8.4.Algorithm Adaptation
• 126- 8.4.1.KNN-based methods
• 127- 8.4.2.Learn the Label Dependencies by the Statistical Models
• 128- 8.5.Evaluation Metrics and Datasets
• 129- 8.5.1.Evaluation Metrics
• 130- 8.5.2.Benchmark Datasets and the Statistics
• 131- 8.6.Chapter Summary
• 132- 9.Privacy Preserving in Data Mining
• 133- 9.1.The K-Anonymity Method
• 134- 9.2.The 1-Diversity Method
• 135- 9.3.The t-Closeness Method
• 136- 9.4.Discussion and Challenges
• 137- 9.5.Chapter Summary
• 138- Contents note continued: pt. III Emerging Applications
• 139- 10.Data Stream
• 140- 10.1.General Data Stream Models
• 141- 10.2.Sampling Approach
• 142- 10.2.1.Random Sampling
• 143- 10.2.2.Cluster Sampling
• 144- 10.3.Wavelet Method
• 145- 10.4.Sketch Method
• 146- 10.4.1.Sliding Window-based Sketch
• 147- 10.4.2.Count Sketch
• 148- 10.4.3.Fast Count Sketch
• 149- 10.4.4.Count Min Sketch
• 150- 10.4.5.Some Related Issues on Sketches
• 151- 10.4.6.Applications of Sketches
• 152- 10.4.7.Advantages and Limitations of Sketch Strategies
• 153- 10.5.Histogram Method
• 154- 10.5.1.Dynamic Construction of Histograms
• 155- 10.6.Discussion
• 156- 10.7.Chapter Summary
• 157- 11.Recommendation Systems
• 158- 11.1.Collaborative Filtering
• 159- 11.1.1.Memory-based Collaborative Recommendation
• 160- 11.1.2.Model-based Recommendation
• 161- 11.2.PLSA Method
• 162- 11.2.1.User Pattern Extraction and Latent Factor Recognition
• 163- 11.3.Tensor Model
• 164- 11.4.Discussion and Challenges
• 165- 11.4.1.Security and Privacy Issues
• 166- 11.4.2.Effectiveness Issue
• 167- 11.5.Chapter Summary
• 168- Contents note continued: 12.Social Tagging Systems
• 169- 12.1.Data Mining and Information Retrieval
• 170- 12.2.Recommender Systems
• 171- 12.2.1.Recommendation Algorithms
• 172- 12.2.2.Tag-Based Recommender Systems
• 173- 12.3.Clustering Algorithms in Recommendation
• 174- 12.3.1.K-means Algorithm
• 175- 12.3.2.Hierarchical Clustering
• 176- 12.3.3.Spectral Clustering
• 177- 12.3.4.Quality of Clusters and Modularity Method
• 178- 12.3.5.K-Nearest-Neighboring
• 179- 12.4.Clustering Algorithms in Tag-Based Recommender Systems
• 180- 12.5.Chapter Summary.

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