New PDF release: Recent Advances In Data Mining Of Enterprise Data:

By T. Warren Liao, Evangelos Triantaphyllou

ISBN-10: 981277985X

ISBN-13: 9789812779854

The most aim of the recent box of information mining is the research of enormous and intricate datasets. a few extremely important datasets can be derived from company and business actions. this sort of facts is called firm info . the typical attribute of such datasets is that the analyst needs to research them for the aim of designing a less expensive approach for optimizing a few form of functionality degree, equivalent to decreasing creation time, bettering caliber, casting off wastes, or maximizing revenue. information during this type could describe diversified scheduling situations in a producing atmosphere, quality controls of a few method, fault prognosis within the operation of a laptop or approach, hazard research while issuing credits to candidates, administration of offer chains in a producing approach, or facts for company comparable decision-making.

Contents:

  • Enterprise information Mining: A evaluation and examine instructions (T W Liao);
  • Application and comparability of type recommendations in Controlling credits threat (L Yu et al.);
  • Predictive class with Imbalanced firm information (S Daskalaki et al.);
  • Data Mining functions of procedure Platform Formation for prime style construction (J Jiao & L Zhang);
  • Multivariate keep an eye on Charts from an information Mining point of view (G C Porzio & G Ragozini);
  • Maintenance making plans utilizing firm facts Mining (L P Khoo et al.);
  • Mining photos of Cell-Based Assays (P Perner);
  • Support Vector Machines and purposes (T B Trafalis & O O Oladunni);
  • A Survey of Manifold-Based studying equipment (X Huo et al.); and different papers.

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Additional info for Recent Advances In Data Mining Of Enterprise Data: Algorithms and Applications

Example text

McDonald (1999) considered data mining as one of the new tools that have accelerated the pace of yield improvement in IC (Integrated Circuit) manufacturing. One data mining application is in “low yield analysis”, which is the investigation of samples of low yield wafers to determine priorities for improvement. Kittler and Wang (1999) described possible uses of data mining in semiconductor manufacturing, which include process and tool control, yield management, and equipment maintenance. Büchner et al.

The knowledge discovered is finally examined by the domain expert(s) and the data mining expert(s) together. This examination of knowledge may lead to the refinement process of data mining. Refinement could take different forms which might include redefining the data, changing the methodology used, refining the parameters of the mining algorithm, etc. Figure 1 summarizes the two processes mentioned above for ease of comparison. 8 Recent Advances in Data Mining of Enterprise Data As proposed by Fayyad et al.

One solution is to reduce data for mining. Data reduction can be achieved in many ways: by feature (or attribute) selection, by discretizing continuous feature-values, and by selecting instances. Feature selection is the process of identifying and removing irrelevant and redundant information as much as possible. Feature selection is important because the inclusion of irrelevant, redundant, and noisy attributes in the model building process can result in poor predictive performance as well as increased computation.

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Recent Advances In Data Mining Of Enterprise Data: Algorithms and Applications by T. Warren Liao, Evangelos Triantaphyllou


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