Mining sequential patterns from large data sets - Info and Reading Options
By Jiong Yang
"Mining sequential patterns from large data sets" was published by Springer in 2005 - New York, it has 160 pages and the language of the book is English.
“Mining sequential patterns from large data sets” Metadata:
- Title: ➤ Mining sequential patterns from large data sets
- Author: Jiong Yang
- Language: English
- Number of Pages: 160
- Publisher: Springer
- Publish Date: 2005
- Publish Location: New York
“Mining sequential patterns from large data sets” Subjects and Themes:
- Subjects: ➤ Computer algorithms - Data mining - Computer science - Computer Communication Networks - Data structures (Computer science) - Database management - Information storage and retrieval systems - Multimedia systems - Data Mining and Knowledge Discovery - Information Storage and Retrieval - Data Structures - Multimedia Information Systems
Edition Specifications:
- Pagination: p. cm.
Edition Identifiers:
- The Open Library ID: OL3311977M - OL19170994W
- Online Computer Library Center (OCLC) ID: 57283889
- Library of Congress Control Number (LCCN): 2004065302
- ISBN-10: 0387242465 - 0387242473
- All ISBNs: 0387242465 - 0387242473
AI-generated Review of “Mining sequential patterns from large data sets”:
"Mining sequential patterns from large data sets" Description:
The Open Library:
The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to find its inherent characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. To meet the different needs of various applications, several models of sequential patterns have been proposed. This volume not only studies the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. Mining Sequential Patterns from Large Data Sets provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable. This book provides an efficient algorithm for mining these patterns. Mining Sequential Patterns from Large Data Sets is designed for a professional audience of researchers and practitioners in industry and also suitable for graduate-level students in computer science.
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