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A Comparison of Model-Based and Fuzzy Clustering Methods
Last modified: 2018-05-16
Abstract
Model-based and fuzzy clustering methods represent widely used approaches
for soft clustering. In the former approach, it is assumed that the data
are generated by a mixture of probability distributions where each component represents a different group or cluster. Each observation unit is ex-post assigned to a cluster using the so-called posterior probability of component membership. In the latter case, no probabilistic assumptions are made and each observation unit belongs to a cluster according to the so-called fuzzy membership degree. The aim of this work is to compare the performance of both approaches by means of a simulation study.
for soft clustering. In the former approach, it is assumed that the data
are generated by a mixture of probability distributions where each component represents a different group or cluster. Each observation unit is ex-post assigned to a cluster using the so-called posterior probability of component membership. In the latter case, no probabilistic assumptions are made and each observation unit belongs to a cluster according to the so-called fuzzy membership degree. The aim of this work is to compare the performance of both approaches by means of a simulation study.
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