Abstract
All three conventional c-means clustering algorithms have their advantages and disadvantages. This paper presents a novel generalized approach to c-means clustering: the objective function is considered to be a mixture of the FCM, PCM, and HCM objective functions. The optimal solution is obtained via evolutionary computation. Our main goal is to reveal the properties of such mixtures and to formulate some rules that yield accurate partitions.
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Szilágyi, L., Iclănzan, D., Szilágyi, S.M., Dumitrescu, D. (2008). GeCiM: A Novel Generalized Approach to C-Means Clustering. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_29
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DOI: https://doi.org/10.1007/978-3-540-85920-8_29
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