Abstract
With the growing demand of categorical data clustering, a new hybrid clustering algorithm, namely Rough set based Fuzzy K-Modes, is proposed in this paper. The principles of rough and fuzzy sets are used in integrated form. It gives the better handling of uncertainty, vagueness, and incompleteness in class definition, while using the concept of lower and upper approximations of rough, on the other hand, the membership function of fuzzy sets enables efficient handling of overlapping partitions. Superiority of the proposed method over state-of-the-art methods is demonstrated quantitatively. For this purpose, two artificial and two real life categorical data sets are used. Also statistical significance test has been carried out to establish the statistical significance of the proposed clustering results.
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Saha, I., Sarkar, J.P., Maulik, U. (2012). Rough Set Based Fuzzy K-Modes for Categorical Data. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_38
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DOI: https://doi.org/10.1007/978-3-642-35380-2_38
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