Domain Driven Data Mining / Edition 1

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Overview

Data mining has emerged as one of the most active areas in information and c- munication technologies(ICT). With the boomingof the global economy,and ub- uitouscomputingandnetworkingacrosseverysectorand business,data andits deep analysis becomes a particularly important issue for enhancing the soft power of an organization, its production systems, decision-making and performance. The last ten years have seen ever-increasingapplications of data mining in business, gove- ment, social networks and the like. However, a crucial problem that prevents data mining from playing a strategic decision-support role in ICT is its usually limited decision-support power in the real world. Typical concerns include its actionability, workability, transferability, and the trustworthy, dependable, repeatable, operable and explainable capabilities of data mining algorithms, tools and outputs. This monograph, Domain Driven Data Mining, is motivated by the real-world challenges to and complexities of the current KDD methodologies and techniques, which are critical issues faced by data mining, as well as the—ndings, thoughts and lessons learned in conducting several large-scale real-world data mining bu- ness applications. The aim and objective of domain driven data mining is to study effective and efficient methodologies, techniques, tools, and applications that can discover and deliver actionable knowledge that can be passed on to business people for direct decision-making and action-taking.

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Editorial Reviews

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“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)

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Product Details

  • ISBN-13: 9781441957368
  • Publisher: Springer US
  • Publication date: 1/20/2010
  • Edition description: 2010
  • Edition number: 1
  • Pages: 248
  • Product dimensions: 6.30 (w) x 9.30 (h) x 0.90 (d)

Table of Contents

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.

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