Mining Sequential Patterns from Large Data Sets by Wei Wang (English) Paperback

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Mining Sequential Patterns from Large Data Sets by Wei Wang, Jiong Yang

The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns.

FORMAT
Paperback LANGUAGE
English CONDITION
Brand New Publisher Description

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 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.

Notes

In many applications, such as bioinformatics, web access traces, and system utilization logs, the data is naturally in the form of sequences. 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. It 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.

Back Cover

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

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

Long Description

In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracting implicit knowledge and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. The techniques can play an important role in understanding data and in capturing intrinsic relationships among data instances. Data mining has been an active research area in the past decade and has been proved to be very useful.

Feature

Contains unique multiple models for various sequential patterns and detailed algorithms on how to mine these patterns. Bio-informaticians not yet familiar with data mining models and algorithms will appreciate the various descriptions of models and algorithms

Description for Sales People

In many applications, such as bioinformatics, web access traces, and system utilization logs, the data is naturally in the form of sequences. 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. It 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.

Details ISBN1441937072 Author Jiong Yang Publisher Springer-Verlag New York Inc. Series Advances in Database Systems Year 2010 ISBN-10 1441937072 ISBN-13 9781441937070 Format Paperback Imprint Springer-Verlag New York Inc. Place of Publication New York, NY Country of Publication United States DEWEY 004 Affiliation Case Western Reserve University, Cleveland, OH Edition 1st Short Title MINING SEQUENTIAL PATTERNS FRO Language English Media Book Series Number 28 Birth 1968 Pages 163 DOI 10.1007/978-0-387-24247-7 Publication Date 2010-12-06 AU Release Date 2010-12-06 NZ Release Date 2010-12-06 US Release Date 2010-12-06 UK Release Date 2010-12-06 Edition Description Softcover reprint of hardcover 1st ed. 2005 Alternative 9780387242460 Illustrations XV, 163 p. Country of Origin NL Product Class Description Computing: Professional & Programming Audience Professional & Vocational
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