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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Cao, Longbing (Editor ), Yu, Philip S. (Editor ), Zhang, Chengqi (Editor ), Zhang, Huaifeng (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer US : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Temas:
Data mining.
Strategic planning.
Leadership.
Information storage and retrieval systems.
Artificial intelligence.
Computer science.
Data Mining and Knowledge Discovery.
Business Strategy and Leadership.
Information Storage and Retrieval.
Artificial Intelligence.
Models of Computation.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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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