Mining Sequential Patterns from Large Data Sets

  • Wei Wang
  • Jiong Yang
Part of the Advances in Database Systems book series (ADBS, volume 28)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Pages 1-3
  3. Pages 5-12
  4. Pages 13-61
  5. Pages 113-160
  6. Pages 161-161
  7. Back Matter
    Pages 163-163

About this book

Introduction

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. 

Keywords

Mathematica algorithms bioinformatics computer science navigation

Authors and affiliations

  • Wei Wang
    • 1
  • Jiong Yang
    • 2
  1. 1.University of North Carolina at Chapel HillChapel HillUSA
  2. 2.Case Western Reserve UniversityClevelandUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b104937
  • Copyright Information Springer Science+Business Media, Inc. 2005
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-24246-0
  • Online ISBN 978-0-387-24247-7
  • Series Print ISSN 1386-2944