Domain Driven Data Mining

Longbing Cao|Philip S. Yu|Chengqi Zhang|Yanchang Zhao

ISBN 10: 1489985077 / ISBN 13: 9781489985071

Verlag: Springer US, 2014

Beschreibung

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Bridges the gap between business expectations and research outputIncludes techniques, methodologies and case studies in real-life enterprise dmAddresses new areas such as blog miningChallenges and Trends.- Methodology.- Ubiquitous I.

Bestandsnummer des Verkäufers 11466730

Bibliografische Details

• Titel: Domain Driven Data Mining
• Verlag: Springer US
• Veröffentlichungsdatum: 2014
• Sprache: Englisch
• ISBN-10: 1489985077
• ISBN-13: 9781489985071
• Bindung: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.

Von der hinteren Coverseite

In the present thriving global economy a need has evolved for complex data analysis to enhance an organization s production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery.

About this book:

• Enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence.

• Examines real-world challenges to and complexities of the current KDD methodologies and techniques.
• Details a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications.
• Bridges the gap between business expectations and research output through detailed exploration of the findings, thoughts and lessons learned in conducting several large-scale, real-world data mining business applications
• Includes techniques, methodologies and case studies in real-life enterprise data mining
• Addresses new areas such as blog mining

Domain Driven Data Mining is suitable for 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.