Titlebook: Data Mining; Special Issue in Ann Robert Stahlbock,Sven F. Crone,Stefan Lessmann Book 2010 Springer-Verlag US 2010 Business Intelligence.Te

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書目名稱Data Mining影響因子(影響力)




書目名稱Data Mining影響因子(影響力)學(xué)科排名




書目名稱Data Mining網(wǎng)絡(luò)公開度




書目名稱Data Mining網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Mining被引頻次




書目名稱Data Mining被引頻次學(xué)科排名




書目名稱Data Mining年度引用




書目名稱Data Mining年度引用學(xué)科排名




書目名稱Data Mining讀者反饋




書目名稱Data Mining讀者反饋學(xué)科排名

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Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closureient implementations based on the antimonotony property of the support. But candidate set generation is still costly and many rules are uninteresting or redundant. In addition one can miss interesting rules like nuggets. We are thus facing a complexity issue and a quality issue..One solution is to g

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An Extended Study of the Discriminant Random Forested decisions. Many approaches have been developed, but one of the most successful in recent times is the random forest. The discriminant random forest is a novel extension of the random forest classification methodology that leverages linear discriminant analysis to performmultivariate node splittin

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Effects of Oversampling Versus Cost-Sensitive Learning for Bayesian and SVM Classifiersling method called “generative oversampling,” which creates new data points by learning parameters for an assumed probability distribution. We then examine theoretically and empirically the effects of different forms of resampling and their relationship to cost-sensitive learning on different classi

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