Abstract
Conventional clustering algorithms categorize an object into precisely one cluster. In many applications, the membership of some of the objects to a cluster can be ambiguous. Therefore, an ability to specify membership to multiple clusters can be useful in real world applications. Fuzzy clustering makes it possible to specify the degree to which a given object belongs to a cluster. In Rough set representations, an object may belong to more than one cluster, which is more flexible than the conventional crisp clusters and less verbose than the fuzzy clusters. The unsupervised nature of fuzzy and rough algorithms means that there is a choice about the level of precision depending on the choice of parameters. This paper describes how one can vary the precision of the rough set clustering and studies its effect on synthetic and real world data sets.
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Lingras, P., Chen, M., Miao, D. (2008). Precision of Rough Set Clustering. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_38
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DOI: https://doi.org/10.1007/978-3-540-88425-5_38
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