Data Mining for Business Applications

Cao, Longbing (EDT); Yu, Philip S. (EDT); Zhang, Chengqi (EDT); Zhang, Huaifeng (EDT)

ISBN 10: 0387794190 / ISBN 13: 9780387794198

Verlag: Springer, 2008

Beschreibung

Bestandsnummer des Verkäufers 5738570-n

Bibliografische Details

• Titel: Data Mining for Business Applications
• Verlag: Springer
• Erscheinungsjahr: 2008
• Sprache: Englisch
• ISBN-10: 0387794190
• ISBN-13: 9780387794198
• Einband: Hardcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

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.

Von der hinteren Coverseite

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 actionable knowledge discovery (AKD)" for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future data mining research and development in the dialogue between academia and business.

Part I centers on developing workable AKD methodologies, including:

• domain-driven data mining
• post-processing rules for actions
• domain-driven customer analytics
• the role of human intelligence in AKD
• maximal pattern-based cluster
• ontology mining
  

Part II focuses on novel KDD domains and the corresponding techniques, exploring the mining of emergent areas and domains such as:

• social security data
• community security data
• gene sequences
• mental health information
• traditional Chinese medicine data
• cancer related data
• blog data
• sentiment information
• web data
• procedures
• moving object trajectories
• land use mapping
• higher education data
• flight scheduling
• algorithmic asset management

Researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management are sure to find this a practical and effective means of enhancing their understanding of and using data mining in their own projects.