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In this chapter we briefly note various studies on and around fuzzy c-means clustering that are not discussed elsewhere in this book.
In section 4.2, we have discussed the use of a similarity measure in fuzzy c-means. In the next section we mention some other methods of fuzzy clustering in which the Euclidean distance or other specific definitions of a dissimilarity measure is unnecessary. Rather, a measure D(x k ,xℓ) can be arbitrary so long as it has the interpretation of the dissimilarity between two objects. Moreover, a function D(x,y) of variables (x,y) is unnecessary and only its value Dkℓ = D(x k ,xℓ) on an arbitrary pair of objects in X is needed. In other words, the algorithm works on the N ×N matrix [Dkℓ] instead of a binary relation D(x,y) on Rp.
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© 2008 Springer-Verlag Berlin Heidelberg
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Miyamoto, S., Ichihashi, H., Honda, K. (2008). Miscellanea. In: Algorithms for Fuzzy Clustering. Studies in Fuzziness and Soft Computing, vol 229. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78737-2_5
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DOI: https://doi.org/10.1007/978-3-540-78737-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78736-5
Online ISBN: 978-3-540-78737-2
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