Abstract
The well-known fuzzy c-means algorithm is an objective function based fuzzy clustering technique that extends the classical k-means method to fuzzy partitions. By replacing the Euclidean distance in the objective function other cluster shapes than the simple (hyper-)spheres of the fuzzy c-means algorithm can be detected, for instance ellipsoids, lines or shells of circles and ellipses. We propose a modified distance function that is based on the dot product and allows to detect a new kind of cluster shape and also lines and (hyper-)planes.
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Klawonn, F., Keller, A. (1999). Fuzzy Clustering Based on Modified Distance Measures. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_25
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DOI: https://doi.org/10.1007/3-540-48412-4_25
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