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Titlebook: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing; 11th International C Aijun An,Jerzy Stefanowski,Guoyin Wang Conference proceedi

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樓主: corrode
31#
發(fā)表于 2025-3-26 22:36:04 | 只看該作者
Data Clustering Algorithms for Information Systemso supervised classification in pattern recognition; in the latter case classification rules should be derived from the classes of given points in a feature space. We also notice that methods of unsupervised classification (in other words, data clustering) in pattern recognition are closely related t
32#
發(fā)表于 2025-3-27 02:35:25 | 只看該作者
33#
發(fā)表于 2025-3-27 05:39:04 | 只看該作者
34#
發(fā)表于 2025-3-27 13:23:50 | 只看該作者
35#
發(fā)表于 2025-3-27 13:45:04 | 只看該作者
36#
發(fā)表于 2025-3-27 21:27:34 | 只看該作者
Information-Theoretic Measure of Uncertainty in Generalized Fuzzy Rough Setsrement of uncertainty in generalized fuzzy rough sets determined by a triangular norm. Based on information theory, the entropy of a generalized fuzzy approximation space is introduced, which is similar to Shannon’s entropy. To measure uncertainty in generalized fuzzy rough sets, a notion of fuzzine
37#
發(fā)表于 2025-3-27 23:31:50 | 只看該作者
38#
發(fā)表于 2025-3-28 02:32:02 | 只看該作者
39#
發(fā)表于 2025-3-28 06:25:25 | 只看該作者
Vaguely Quantified Rough Setstive way. As a result, the hybrid theory inherits their respective strengths, but also exhibits some weaknesses. In particular, although they allow for gradual membership, fuzzy rough sets are still abrupt in a sense that adding or omitting a single element may drastically alter the outcome of the a
40#
發(fā)表于 2025-3-28 13:31:18 | 只看該作者
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