Description
Domain Driven Data Mining, 2010
Authors: Cao Longbing, Yu Philip S., Zhang Chengqi, Zhao Yanchang
Language: EnglishSubjects for Domain Driven Data Mining:
Approximative price 105.49 €
In Print (Delivery period: 15 days).
Add to cart the book of Cao Longbing, Yu Philip S., Zhang Chengqi, Zhao Yanchang
Domain Driven Data Mining
Publication date: 12-2014
248 p. · 15.5x23.5 cm · Paperback
Publication date: 12-2014
248 p. · 15.5x23.5 cm · Paperback
Approximative price 105.49 €
In Print (Delivery period: 15 days).
Add to cart the book of Cao Longbing, Yu Philip S., Zhang Chengqi, Zhao Yanchang
Domain driven data mining
Publication date: 01-2010
248 p. · 15.5x23.5 cm · Hardback
Publication date: 01-2010
248 p. · 15.5x23.5 cm · Hardback
Description
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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. 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.
Challenges and Trends.- Methodology.- Ubiquitous Intelligence.- Knowledge Actionability.- AKD Frameworks.- Combined Mining.- Agent-Driven Data Mining.- Post Mining.- Mining Actionable Knowledge on Capital Market Data.- Mining Actionable Knowledge on Social Security Data.- Open Issues and Prospects.- Reading Materials.
Bridges the gap between business expectations and research output
Includes techniques, methodologies and case studies in real-life enterprise dm
Addresses new areas such as blog mining
Includes supplementary material: sn.pub/extras
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