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Advances in Database Systems #28: Mining Sequential Patterns from Large Data Sets

by

Advances in Database Systems #28: Mining Sequential Patterns from Large Data Sets Cover

 

Synopses & Reviews

Publisher Comments:

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. 

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.

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

List of Figures.- List of Tables.- Preface.- Introduction.- Related Work.- Period.- Statistically Significant Patterns.- Approximate Patterns.- Conclusion Remark.- Index.

Product Details

ISBN:
9781441937070
Author:
Wang, Wei
Publisher:
Springer
Author:
Yang, Jiong
Location:
Boston, MA
Subject:
Data Mining and Knowledge Discovery
Subject:
Database management
Subject:
Information storage and retrieval.
Subject:
Data structures
Subject:
multimedia information systems
Subject:
Computer Communication Networks
Subject:
Computers-Reference - General
Subject:
Computer Science
Subject:
B
Subject:
Data mining
Subject:
Information storage and retrieva
Subject:
Data structures (Computer scienc
Subject:
Multimedia systems
Copyright:
Edition Description:
Softcover reprint of hardcover 1st ed. 2005
Series:
Advances in Database Systems
Series Volume:
28
Publication Date:
20101206
Binding:
TRADE PAPER
Language:
English
Pages:
180
Dimensions:
235 x 155 mm 285 gr

Related Subjects

Computers and Internet » Computers Reference » General
Computers and Internet » Internet » Information
Computers and Internet » Networking » General
Computers and Internet » Personal Computers » General

Advances in Database Systems #28: Mining Sequential Patterns from Large Data Sets New Trade Paper
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Product details 180 pages Not Avail - English 9781441937070 Reviews:
"Synopsis" by , 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.

"Synopsis" by , 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. 
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