Abstract
The fuzzy clustering methods are useful in the data mining applications. This paper describes a new fuzzy clustering method in which each cluster prototype is calculated as a value that minimizes introducted generalized cost function. The generalized cost function utilizes the L p norm. The fuzzy meridian is a special case of cluster prototype for p = 2 as well as the fuzzy meridian for p = 1. A method for the norm selection is proposed. An example illustrating the performance of the proposed method is given.
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Przybyła, T., Jeżewski, J., Horoba, K., Roj, D. (2011). Hybrid Fuzzy Clustering Using L P Norms. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6591. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20039-7_19
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DOI: https://doi.org/10.1007/978-3-642-20039-7_19
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