Domain Driven 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.

From the reviews:
“This book offers a comprehensive discussion of domain-driven data mining (D3M), a set of techniques and methodologies that aim to discover actionable knowledge that can be presented to business decision makers in order to enable them to make informed decisions. … The resulting approach is an exploration of possibilities for enhancing the decision-support power of data mining and knowledge discovery. … This well-written and practical book summarizes domain-specific problem-solving methods for the delivery of actionable knowledge, and is suitable for researchers and students … .” (Alessandro Berni, ACM Computing Reviews, November, 2010)

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.

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