Titlebook: Rough Sets and Current Trends in Computing; 8th International Co JingTao Yao,Yan Yang,Lech Polkowski Conference proceedings 2012 Springer-V

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青春期

ENDOW

A Refined Rough k-Means Clustering with Hybrid Threshold and distance threshold. And then, we refine the algorithm for assigning objects based on the hybrid thresholds to ensure that the outliers in inline positions and rectangle positions to be represented reasonably. At last, some experiments are provided to compare this refined RCM with the original RCM.

貞潔

An Incremental Approach for Updating Approximations of Rough Fuzzy Sets under the Variation of the Ods for updating approximations in rough set theory and its extension has been received much attention recently. This paper presents an approach for incrementally updating approximations of fuzzy rough sets in dynamic fuzzy decision systems when a single object immigrating and emigrating. Examples are employed to illustrate the proposed approach.

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Induction of Ordinal Classification Rules from Incomplete Data-based Rough Set Approach (DRSA). We show how to induce classification rules in a way that has desirable properties. Our considerations are extended to an experimental comparison of the postulated rule classifier with other ordinal and non-ordinal classifiers.

蚊子

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Inference Processes Using Incomplete Knowledge in Decision Support Systems – Chosen Aspectsecision support systems with incomplete knowledge. The authors also wish to inference within such decision support systems using rules, of which premises are not fully covered by the facts. The AHC or mAHC algorithm is used. The authors adapted Salton’s most promising path method with own modifications for a fast look-up of the rules.

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鑲嵌細(xì)工

https://doi.org/10.1007/978-3-642-32115-3approximation; classification rules; decision support systems; machine learning; set theory

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