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Computer Science - Database Management & Information Retrieval | Mining Sequential Patterns from Large Data Sets

Mining Sequential Patterns from Large Data Sets

Series: Advances in Database Systems, Vol. 28

Wang, Wei, Yang, Jiong

2005, XV, 163 p.

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  • About this book

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. 

Content Level » Professional/practitioner

Keywords » Mathematica - algorithms - bioinformatics - computer science - navigation

Related subjects » Communication Networks - Database Management & Information Retrieval - Information Systems and Applications - Theoretical Computer Science

Table of contents / Sample pages 

Related Work.- Periodic Patterns.- Statistically Significant Patterns.- Approximate Patterns.- Conclusion Remark.

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