Abstract
In this paper we propose a rough sets-based clustering method that takes simplicity of the resultant classification knowledge into account. The method uses a new measure called indiscernibility degree. Indiscernibility degree of two objects corresponds to the ratio of equivalence relations that commonly regard the two objects as indiscernible ones. If an equivalence relation has ability to discern the two objects that have high indiscernibility degree, it can be considered to give too fine classification to the objects. Such an equivalence relation is then modified to treat the two objects as indiscernible ones. Consequently, we obtain the clusters that can be described with simples set of classification rules.
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Hirano, S., Tsumoto, S. (2003). A Knowledge-Oriented Clustering Method Based on Indiscernibility Degree of Objects. In: Inuiguchi, M., Hirano, S., Tsumoto, S. (eds) Rough Set Theory and Granular Computing. Studies in Fuzziness and Soft Computing, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36473-3_15
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DOI: https://doi.org/10.1007/978-3-540-36473-3_15
Publisher Name: Springer, Berlin, Heidelberg
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