Data Mining for Business Applications
Springer-Verlag New York Inc.
978-1-4419-4635-5 (ISBN)
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.
Zusatzinfo | XX, 302 p. |
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Verlagsort | New York, NY |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Informatik ► Office Programme ► Outlook | |
Informatik ► Theorie / Studium ► Algorithmen | |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
ISBN-10 | 1-4419-4635-7 / 1441946357 |
ISBN-13 | 978-1-4419-4635-5 / 9781441946355 |
Zustand | Neuware |
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