Data Mining for Business Applications

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Cao, Longbing (Επιμελητής έκδοσης), Yu, Philip S. (Επιμελητής έκδοσης), Zhang, Chengqi (Επιμελητής έκδοσης), Zhang, Huaifeng (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2009.
• Θέματα: Computer science.; Leadership.; Computers.; Data mining.; Information storage and retrieval.; Artificial intelligence.; Computer Science.; Data Mining and Knowledge Discovery.; Business Strategy/Leadership.; Information Storage and Retrieval.; Artificial Intelligence (incl. Robotics).; Computing Methodologies.; Models and Principles.

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...

Πίνακας περιεχομένων:

• 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.