Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Granular reasoning using zooming in & out: part 1. propositional reasoning
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Discernibility-based variable granularity and kansei representations
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
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According to the sizes of the attribute set and the information table, the information tables are categorized into three types of Rough Set problems, Pattern Recognition/Machine Learning problems, and Statistical Model Identification problems. In the first Rough Set situation, what we have seen is as follows: 1) The ”granularity” should be taken so as to divide equally the unseen tuples out of the information table, 2) The traditional ”Reduction” sense accords with the above insistence, and 3) The ”stable” subsets of tuples, which are defined through a ”Galois connection” between the subset and the corresponding attribute subset, may play an important role to capture some characteristics that can be read from the given information table. We show these with some illustrative examples.