Abstract
This paper presents a new indiscernibility-based clustering method called rough clustering, that works on relative proximity. Our method lies its basis on iterative refinement of N binary classifications, where N denotes the number of objects. First, for each of N objects, an equivalence relation that classifies all the other objects into two classes, similar and dissimilar, is assigned by referring to their relative proximity. Next, for each pair of the objects, we count the number of binary classifications in which the pair is included in the same class. We call this number as indiscernibility degree. If the indiscernibility degree of a pair is larger than a user-defined threshold value, we modify the equivalence relations so that all of them commonly classify the pair into the same class. This process is repeated until class assignment becomes stable. Consequently, we obtain the clustering result that follows given level of granularity without using geometric measures.
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S. Hirano and S. Tsumoto (2003): An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations — Rough Clustering —,” Journal of Advanced Computational Intelligence and Intelligent Informatics, (in press).
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© 2003 Springer-Verlag Berlin Heidelberg
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Hirano, S., Tsumoto, S. (2003). Indiscernibility-Based Clustering: Rough Clustering. In: Bilgiç, T., De Baets, B., Kaynak, O. (eds) Fuzzy Sets and Systems — IFSA 2003. IFSA 2003. Lecture Notes in Computer Science, vol 2715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44967-1_45
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DOI: https://doi.org/10.1007/3-540-44967-1_45
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