Abstract
Fuzzy clustering approaches such as fuzzy c-means which is a fuzzified version of k-means method have been developed in order to deal with vague cluster memberships and used widely. Likewise, rough set approaches such as rough k-means and rough set k-means are also considered to be effective. In this paper, we propose the Rough Membership k-Means (RMKM) clustering in which values of the rough membership function are used as fuzzy cluster memberships. Furthermore, we carried out some numerical experiments in order to demonstrate the performance of the rough membership k-means clustering.
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Acknowledgment
This work is supported by Program to Disseminate Tenure Tracking System, MEXT, Japan.
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Ubukata, S., Notsu, A., Honda, K. (2016). The Rough Membership k-Means Clustering. In: Huynh, VN., Inuiguchi, M., Le, B., Le, B., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2016. Lecture Notes in Computer Science(), vol 9978. Springer, Cham. https://doi.org/10.1007/978-3-319-49046-5_18
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DOI: https://doi.org/10.1007/978-3-319-49046-5_18
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