Titlebook: Advances in Knowledge Discovery and Data Mining; 9th Pacific-Asia Con Tu Bao Ho,David Cheung,Huan Liu Conference proceedings 2005 Springer-

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Clinical Atlas of Ocular Oncologyas the model generated by SVM is like a black box, it is difficult for user to interpret and understand how the model makes its decision. In this paper, a hyperrectangle rules extraction (HRE) algorithm is proposed to extract rules from trained SVM. Support vector clustering (SVC) algorithm is used

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Abdulrahman H. Algaeed,Igor Kozaking only by chance. Techniques are developed for automatically discarding statistically insignificant exploratory rules that cannot survive a hypothesis with regard to its ancestors. We call such . rules .. In this paper, we argue that there is another type of derivative exploratory rules, which is

乞討

Abdulrahman H. Algaeed,Igor Kozaks may be derived from even reasonably sized real-life databases. A possible solution consists in using results of Formal Concept Analysis to generate a generic base of association rules. This set, of reduced size, makes it possible to derive all the association rules via an adequate axiomatic system

BILL

Long-Standing Retinal Detachments,dge extraction techniques are devised for the discovery of compact and lossless knowledge formally expressed by generic bases. In this paper, we present an approach for deriving generic bases of association rules. Using this approach, we construct small partially ordered sub-structures. Then, these

Kinetic

Long-Standing Retinal Detachments,find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. We propose “Apriori-Inverse”, a method of discovering sporadic rules by ignoring all candidate itemsets above a maximum support threshold. We define

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