Mining Sequential Patterns from Large Data Sets (Advances in Database Systems, 28)

Wang, Wei; Yang, Jiong

ISBN 10: 1441937072 ISBN 13: 9781441937070

Editorial: Springer, 2010

Idioma: Ingl�s

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Descripci�n

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Detalles bibliogr�ficos

T�tulo

Mining Sequential Patterns from Large Data Sets (Advances in Database Systems, 28)

Editor

Springer

Fecha de publicaci�n

2010

Idioma

Ingl�s

ISBN 10

1441937072

ISBN 13

9781441937070

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

New

Cat�logos de vendedores

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Sinopsis

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.

De la contraportada

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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