Abstract
A new cluster validity index is proposed to determine the optimal number of clusters for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index exploits an overlap measure and a separation measure between clusters. A good fuzzy partition is expected to have a low degree of overlap and a larger separation distance. Testing of the proposed index on well-known data sets showed its superior effectiveness and reliability in comparison to other indexes.
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© 2003 Springer-Verlag Berlin Heidelberg
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Kim, DW., Lee, K.H. (2003). Validation of Fuzzy Partitions Obtained through Fuzzy C-Means Clustering. In: Zhong, N., RaÅ›, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_59
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DOI: https://doi.org/10.1007/978-3-540-39592-8_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20256-1
Online ISBN: 978-3-540-39592-8
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