Mining Sequential Patterns from Large Data Sets / Edition 1

Mining Sequential Patterns from Large Data Sets / Edition 1

by Wei Wang, Jiong Yang
     
 

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

See more details below

Overview

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.

Read More

Product Details

ISBN-13:
9781441937070
Publisher:
Springer US
Publication date:
12/06/2010
Series:
Advances in Database Systems Series, #28
Edition description:
Softcover reprint of hardcover 1st ed. 2005
Pages:
163
Product dimensions:
0.38(w) x 6.14(h) x 9.21(d)

Table of Contents

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

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >