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Data Mining for Business Applications

Overview of attention for book
Cover of 'Data Mining for Business Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Domain Driven Data Mining
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    Chapter 2 Post-processing Data Mining Models for Actionability
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    Chapter 3 On Mining Maximal Pattern-Based Clusters
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    Chapter 4 Role of Human Intelligence in Domain Driven Data Mining
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    Chapter 5 Ontology Mining for Personalized Search
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    Chapter 6 Data Mining Applications in Social Security
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    Chapter 7 Security Data Mining: A Survey Introducing Tamper-Resistance
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    Chapter 8 A Domain Driven Mining Algorithm on Gene Sequence Clustering
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    Chapter 9 Data Mining for Business Applications
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    Chapter 10 Data Mining for Business Applications
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    Chapter 11 Microarray Data Mining: Selecting Trustworthy Genes with Gene Feature Ranking
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    Chapter 12 Blog Data Mining for Cyber Security Threats
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    Chapter 13 Blog Data Mining: The Predictive Power of Sentiments
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    Chapter 14 Web Mining: Extracting Knowledge from the World Wide Web
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    Chapter 15 DAG Mining for Code Compaction
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    Chapter 16 A Framework for Context-Aware Trajectory
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    Chapter 17 Census Data Mining for Land Use Classification
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    Chapter 18 Visual Data Mining for Developing Competitive Strategies in Higher Education
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    Chapter 19 Data Mining For Robust Flight Scheduling
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    Chapter 20 Data Mining for Algorithmic Asset Management
Attention for Chapter 5: Ontology Mining for Personalized Search
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Citations

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Chapter title
Ontology Mining for Personalized Search
Chapter number 5
Book title
Data Mining for Business Applications
Published by
Springer, Boston, MA, January 2009
DOI 10.1007/978-0-387-79420-4_5
Book ISBNs
978-0-387-79419-8, 978-0-387-79420-4
Authors

Yuefeng Li, Xiaohui Tao, Li, Yuefeng, Tao, Xiaohui

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 33%
Researcher 1 33%
Student > Doctoral Student 1 33%
Readers by discipline Count As %
Unspecified 1 33%
Computer Science 1 33%
Engineering 1 33%

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