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