Abstract
Clustering is defining an equivalence relation between the samples in nature, and two samples are equivalent if they belong to one class. The rough and fine of the granularity reflect the similarity threshold in clustering. In this paper, the disadvantage of granular analysis in clustering based on the theory quotient space, which can’t solve the problem when there are intersections between classes, is pointed out, and the theory of fuzzy tolerance quotient space is introduced, and granular analysis in clustering based on the theory of fuzzy tolerance quotient space is presented. The results of the experiment about clustering radio communication signals show the efficiency of the algorithm.
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Wang, L., Wang, L., Wu, Z. (2010). Granular Analysis in Clustering Based on the Theory of Fuzzy Tolerance Quotient Space. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_100
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DOI: https://doi.org/10.1007/978-3-642-16248-0_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16247-3
Online ISBN: 978-3-642-16248-0
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