Data Mining for Business Applications

✍ Scribed by Cao Longbing (auth.), Longbing Cao, Philip S. Yu, Chengqi Zhang, Huaifeng Zhang (eds.)

• Publisher: Springer US
• Year: 2009
• Tongue: English
• Leaves: 310
• Edition: 1
• Category: Library

✦ Synopsis

Data Mining for Business Applications presents state-of-the-art data mining research and development related to methodologies, techniques, approaches and successful applications. The contributions of this book mark a paradigm shift from "data-centered pattern mining" to "domain-driven actionable knowledge discovery (AKD)" for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future data mining research and development in the dialogue between academia and business.

Part I centers on developing workable AKD methodologies, including:

• domain-driven data mining
• post-processing rules for actions
• domain-driven customer analytics
• the role of human intelligence in AKD
• maximal pattern-based cluster
• ontology mining
  

Part II focuses on novel KDD domains and the corresponding techniques, exploring the mining of emergent areas and domains such as:

• social security data
• community security data
• gene sequences
• mental health information
• traditional Chinese medicine data
• cancer related data
• blog data
• sentiment information
• web data
• procedures
• moving object trajectories
• land use mapping
• higher education data
• flight scheduling
• algorithmic asset management

Researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management are sure to find this a practical and effective means of enhancing their understanding of and using data mining in their own projects.

✦ Table of Contents

Front Matter....Pages i-xix
Introduction to Domain Driven Data Mining....Pages 3-10
Post-processing Data Mining Models for Actionability....Pages 11-30
On Mining Maximal Pattern-Based Clusters....Pages 31-52
Role of Human Intelligence in Domain Driven Data Mining....Pages 53-61
Ontology Mining for Personalized Search....Pages 63-78
Data Mining Applications in Social Security....Pages 81-96
Security Data Mining: A Survey Introducing Tamper-Resistance....Pages 97-110
A Domain Driven Mining Algorithm on Gene Sequence Clustering....Pages 111-126
Domain Driven Tree Mining of Semi-structured Mental Health Information....Pages 127-141
Text Mining for Real-time Ontology Evolution....Pages 143-157
Microarray Data Mining: Selecting Trustworthy Genes with Gene Feature Ranking....Pages 159-168
Blog Data Mining for Cyber Security Threats....Pages 169-182
Blog Data Mining: The Predictive Power of Sentiments....Pages 183-195
Web Mining: Extracting Knowledge from the World Wide Web....Pages 197-208
DAG Mining for Code Compaction....Pages 209-223
A Framework for Context-Aware Trajectory....Pages 225-239
Census Data Mining for Land Use Classification....Pages 241-251
Visual Data Mining for Developing Competitive Strategies in Higher Education....Pages 253-266
Data Mining For Robust Flight Scheduling....Pages 267-282
Data Mining for Algorithmic Asset Management....Pages 283-295
Back Matter....Pages 297-302

✦ Subjects

Data Mining and Knowledge Discovery; Information Storage and Retrieval; Artificial Intelligence (incl. Robotics); Computing Methodologies; Models and Principles