Domain Driven Data Mining by Longbing Cao

By Longbing Cao

In the current thriving international economic climate a necessity has advanced for complicated facts research to reinforce an organization’s construction structures, decision-making strategies, and function. In flip, facts mining has emerged as the most lively parts in info applied sciences. Domain pushed facts Mining bargains state-of the-art study and improvement results on methodologies, innovations, methods and profitable functions in area pushed, actionable wisdom discovery.

About this book:

  • Enhances the actionability and wider deployment of present data-centered facts mining via a mix of area and enterprise orientated elements, constraints and intelligence.
  • Examines real-world demanding situations to and complexities of the present KDD methodologies and techniques.
  • Details a paradigm shift from "data-centered development mining" to "domain pushed actionable wisdom discovery" for next-generation KDD study and purposes.
  • Bridges the distance among enterprise expectancies and study output via precise exploration of the findings, suggestions and classes realized in engaging in numerous large-scale, real-world facts mining company applications
  • Includes concepts, methodologies and case experiences in real-life company information mining
  • Addresses new parts resembling web publication mining

Domain pushed info Mining is appropriate for researchers, practitioners and collage scholars within the components of knowledge mining and information discovery, wisdom engineering, human-computer interplay, man made intelligence, clever info processing, choice help structures, wisdom administration, and KDD undertaking management.

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The concept map consists of the following layers from the outer most layer to the central core. L. 1007/978-1-4419-5737-5_2, © Springer Science+Business Media, LLC 2010 27 2 D3 M Methodology 28 • Specific domain problems: In general, this can apply to any domain problems from retail to government to social network, from either a sector or specific business problem perspective. However, since D3 M mainly targets complex knowledge from complex data, we do not concern ourselves with those problems and businesses that have been or can be well-handled by existing data mining and knowledge discovery techniques.

Technical significance is usually defined in a straightforward manner by reflecting the significance of the findings in terms of the utilized techniques. Consequently, pattern interestingness is measured in terms of such technical metrics. info 36 2 D3 M Methodology end. In many cases, business people may just find them unconvincing, unjustifiable, unacceptable, impractical and inoperable. Such situations have hindered the deployment and adoption of data mining in real applications. Therefore it is essentially critical to develop pattern interestingness catering for business concerns, preferences and expectations.

For instance, constraints may include domain constraints, data constraints, interestingness constraints, deployment constraints and deliverable constraints. To deal with constraints, various strategies and methods may be undertaken; for instance, interestingness constraints are modeled in terms of interestingness measures and factors, such as objective interestingness and subjective interestingness. In a summary, we list ubiquitous intelligence hidden and explicitly existing in domain problems in terms of the following major aspects.

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