Abstract
Pawlak’s indiscernibility relation (which is an equivalence relation) represents a limit of our knowledge embedded in an information system. In many cases covering approximation spaces rely on tolerance relations instead of equivalence relations. In real practice (for example in data mining) tolerance relations may be generated from the properties of objects. A given tolerance relation represents similarity between objects, but the usage of similarity is very special: it emphasizes the similarity to a given object and not the similarity of objects ‘in general’. The authors show that this usage has some problematic consequences. The main goal of the paper is to show that if one uses the method of correlation clustering then there is a way to construct a general (partial) approximation space with disjoint base sets relying on the similarity of objects generated by their properties. At the end a software describing a real life problem is presented.
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Nagy, D., Mihálydeák, T., Aszalós, L. (2017). Similarity Based Rough Sets. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_7
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