2 research outputs found
Extended Fuzzy Clustering Algorithms
Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing. Despitethe successful applications, there are a number of issues that must be dealt with in practicalapplications of fuzzy clustering algorithms. This technical report proposes two extensionsto the objective function based fuzzy clustering for dealing with these issues. First, the(point) prototypes are extended to hypervolumes whose size is determined automaticallyfrom the data being clustered. These prototypes are shown to be less sensitive to a biasin the distribution of the data. Second, cluster merging by assessing the similarity amongthe clusters during optimization is introduced. Starting with an over-estimated number ofclusters in the data, similar clusters are merged during clustering in order to obtain a suitablepartitioning of the data. An adaptive threshold for merging is introduced. The proposedextensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resultingextended algorithms are given. The properties of the new algorithms are illustrated invarious examples.fuzzy clustering;cluster merging;similarity;volume prototypes
Extended Fuzzy Clustering Algorithms
Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It
has been applied successfully in various fields including finance and marketing. Despite
the successful applications, there are a number of issues that must be dealt with in practical
applications of fuzzy clustering algorithms. This technical report proposes two extensions
to the objective function based fuzzy clustering for dealing with these issues. First, the
(point) prototypes are extended to hypervolumes whose size is determined automatically
from the data being clustered. These prototypes are shown to be less sensitive to a bias
in the distribution of the data. Second, cluster merging by assessing the similarity among
the clusters during optimization is introduced. Starting with an over-estimated number of
clusters in the data, similar clusters are merged during clustering in order to obtain a suitable
partitioning of the data. An adaptive threshold for merging is introduced. The proposed
extensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting
extended algorithms are given. The properties of the new algorithms are illustrated in
various examples