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Mining Sequential Patterns from Large Data Sets

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...

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Bibliographic Details
Main Authors: Wang, Wei, Yang, Jiong (Author)
Format: eBook
Language:English
Published: New York, NY Springer US 2005, 2005
Edition:1st ed. 2005
Series:Advances in Database Systems
Subjects:
Computer Communication Networks
Artificial Intelligence / Data Processing
Multimedia Systems
Information Storage And Retrieval
Database Management
Data Mining
Computer Networks 
Information Storage And Retrieval Systems
Data Mining And Knowledge Discovery
Multimedia Information Systems
Data Science
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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520 |a 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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