Data Mining for Business Applications

• Corporate Author: SpringerLink (Online service)
• Other Authors: Cao, Longbing. (Editor), Yu, Philip S. (Editor), Zhang, Chengqi. (Editor), Zhang, Huaifeng. (Editor)
• Format: Electronic
• Language: English
• Published: Boston, MA : Springer US, 2009.
• Subjects: Computer science.; Data mining.; Information storage and retrieval systems.; Electronic data processing.; Artificial intelligence.; Computer Science.; Data Mining and Knowledge Discovery.; Information Storage and Retrieval.; Artificial Intelligence (incl. Robotics).; Computing Methodologies.; Models and Principles.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-0-387-79420-4

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...

Table of Contents:

• Part I Domain Driven KDD Methodology: Introduction 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
• Part II 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 WorldWideWeb
• DAG Mining for Code Compaction
• A Framework for Context-Aware Trajectory Data Mining
• 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
• References
• Reviewer List
• Index.