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Mining Sequential Patterns from Large Data Sets

Name: Mining Sequential Patterns from Large Data Sets
Brand: Springer, Berlin
SKU: 9781441937070
Price: 553.98 RON
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Autor: Wei Wang, Jiong Yang

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Editura: Springer, Berlin

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The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining. In man...

Informații despre carte

Autor

Wei Wang, Jiong Yang

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2010

Pagini

163

EAN

9781441937070

ISBN

1441937072

Enbook ID

01422278

Editura

Springer, Berlin

Greutate

262

Dimensiuni

155 x 235 x 9

Descriere completă

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.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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