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Advances in Database Systems #33: Sequence Data Mining

by

Advances in Database Systems #33: Sequence Data Mining Cover

 

Synopses & Reviews

Publisher Comments:

Understanding sequence data, and the ability to utilize this hidden knowledge, creates a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering. Forward by Professor Jiawei Han, University of Illinois at Urbana-Champaign.  

Synopsis:

This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods.

Synopsis:

Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more.

This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.

Table of Contents

-Forward by Professor Jiawei Han, University of Illinois at Urbana-Champaign.- Preface.- Introduction.- Frequent and Closed Sequence Patterns.- Classification, Clustering, Features and Distances of Sequence Data.- Sequence Motifs: Identifying and Characterizing Sequence Families.- Mining Partial Orders from Sequences.- Distinguishing Sequence Patterns.- Related Topics.- Index.- References.  

Product Details

ISBN:
9780387699363
Author:
Dong, Guozhu
Publisher:
Springer
Author:
Pei, Jian
Subject:
Networking - General
Subject:
Database Management - General
Subject:
Information Storage & Retrieval
Subject:
Database Management - Database Mining
Subject:
System Administration - Storage & Retrieval
Subject:
Bioengineering
Subject:
Bioinformatics
Subject:
Data analysis
Subject:
Genomics
Subject:
pattern mining
Subject:
pattern types
Subject:
sequence patterns
Subject:
Web services
Subject:
Data Mining and Knowledge Discovery
Subject:
Information storage and retrieval.
Subject:
Database management
Subject:
Computational Biology/Bioinformatics
Subject:
biometrics
Subject:
Computer Communication Networks <P>This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis
Subject:
Computers-Reference - General
Subject:
Computer Communication Networks
Copyright:
Edition Description:
Book
Series:
Advances in Database Systems
Series Volume:
33
Publication Date:
20070809
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
168
Dimensions:
235 x 155 mm 920 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Internet » Information
Computers and Internet » Networking » General
Reference » Science Reference » Technology
Science and Mathematics » Chemistry » Chemical Engineering
Science and Mathematics » Mathematics » Topology
Science and Mathematics » Physics » Astrophysics

Advances in Database Systems #33: Sequence Data Mining New Hardcover
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$180.25 In Stock
Product details 168 pages Springer - English 9780387699363 Reviews:
"Synopsis" by , This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods.
"Synopsis" by , Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more.

This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.

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