Mining Sequential Patterns from Large Data Sets

✍ Scribed by Wei Wang, Jiong Yang (auth.)

Publisher: Springer US
Year: 2005
Tongue: English
Leaves: 174
Series: Advances in Database Systems 28
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Introduction....Pages 1-3
Related Work....Pages 5-12
Periodic Patterns....Pages 13-61
Statistically Significant Patterns....Pages 63-112
Approximate Patterns....Pages 113-160
Conclusion Remark....Pages 161-161

✦ Subjects

Data Mining and Knowledge Discovery;Database Management;Information Storage and Retrieval;Data Structures;Multimedia Information Systems;Computer Communication Networks

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