Abstract
In this paper, an unsupervised Mercer kernel based fuzzy c-means (MKFCM) clustering algorithm is proposed, in which the implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. A new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results have demonstrated the promising performance of the MKFCM algorithm in different scenarios.
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Zhou, S., Gan, J.Q. (2004). Mercer Kernel, Fuzzy C-Means Algorithm, and Prototypes of Clusters. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_90
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DOI: https://doi.org/10.1007/978-3-540-28651-6_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
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