Items related to Domain Driven Data Mining
Synopsis
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 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.
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From the Back Cover
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.
Review
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)"About this title" may belong to another edition of this title.
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