Data Mining for Business Applications
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...
| Other Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer US
2009, 2009
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| Edition: | 1st ed. 2009 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Domain Driven KDD Methodology
- to Domain Driven Data Mining
- Post-processing Data Mining Models for Actionability
- On Mining Maximal Pattern-Based Clusters
- Role of Human Intelligence in Domain Driven Data Mining
- Ontology Mining for Personalized Search
- Novel KDD Domains & Techniques
- Data Mining Applications in Social Security
- Security Data Mining: A Survey Introducing Tamper-Resistance
- A Domain Driven Mining Algorithm on Gene Sequence Clustering
- Domain Driven Tree Mining of Semi-structured Mental Health Information
- Text Mining for Real-time Ontology Evolution
- Microarray Data Mining: Selecting Trustworthy Genes with Gene Feature Ranking
- Blog Data Mining for Cyber Security Threats
- Blog Data Mining: The Predictive Power of Sentiments
- Web Mining: Extracting Knowledge from the World Wide Web
- DAG Mining for Code Compaction
- A Framework for Context-Aware Trajectory
- Census Data Mining for Land Use Classification
- Visual Data Mining for Developing Competitive Strategies in Higher Education
- Data Mining For Robust Flight Scheduling
- Data Mining for Algorithmic Asset Management