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Mining sequential patterns from large data sets. (English) Zbl 1067.68057

The Kluwer International Series on Advances in Database Systems 28. New York, NY: Springer (ISBN 0-387-24246-5/hbk). xv, 163 p. (2005).
Publisher’s description: The focus of this book 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.
The book 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. It provides an efficient algorithm for mining these patterns.
The book is designed for a professional audience of researchers and practitioners in industry, and also suitable for graduate-level students in computer science.

MSC:

68P05 Data structures
68-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science
68P20 Information storage and retrieval of data
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