Fuzzy clustering extends crisp clustering in the sense that objects can belong to various clusters with different membership degrees at the same time, whereas crisp or deterministic clustering assigns each object to a unique cluster. The standard approach to fuzzy clustering introduces the so-called fuzzifier which controls how much clusters may overlap. In this paper we illustrate, how this fuzzifier can help to reduce the number of undesired local minima of the objective function that is associated with fuzzy clustering. Apart from this advantage, the fuzzifier has also some drawbacks that are discussed in this paper. A deeper analysis of the fuzzifier concept leads us to a more general approach to fuzzy clustering that can overcome the problems caused by the fuzzifier.
@article{urn:eudml:doc:39267,
title = {Fuzzy clustering: Insights and new approach.},
journal = {Mathware and Soft Computing},
volume = {11},
year = {2004},
pages = {125-142},
zbl = {1105.68414},
mrnumber = {MR2139293},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39267}
}
Klawonn, Frank. Fuzzy clustering: Insights and new approach.. Mathware and Soft Computing, Tome 11 (2004) pp. 125-142. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39267/