Data Mining for Business Applications

Longbing Cao

ISBN 10: 1441946357 / ISBN 13: 9781441946355

Editore: Springer US, Springer US Nov 2010, 2010

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This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 324 pp. Englisch.

Codice articolo 9781441946355

Dati bibliografici

• Titolo: Data Mining for Business Applications
• Editore: Springer US, Springer US Nov 2010
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 1441946357
• ISBN 13: 9781441946355
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 18 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 18 millimetri di profondità
• Peso: 493 grammi

Descrizione articolo

Riassunto

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.

Dalla quarta di copertina

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.