Abstract
Possibilistic clustering that is robust to noise in data is another useful tool in addition to the best-known fuzzy c-means. However, there is a fundamental problem of strong dependence on initial values in possibilistic clustering and there is a proposal of an algorithm generating ‘one cluster at a time.’ Moreover this method is related to the mountain clustering algorithm. In this paper these features are reconsidered and a number of algorithms of sequential generation of clusters which includes a possibilistic medoid clustering are proposed. These algorithms automatically determine the number of clusters. An illustrative example with different methods of sequential clustering is given.
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Miyamoto, S., Kuroda, Y. (2007). Algorithms for Sequential Extraction of Clusters by Possibilistic Clustering. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2007. Lecture Notes in Computer Science(), vol 4617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73729-2_22
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DOI: https://doi.org/10.1007/978-3-540-73729-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73728-5
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