Abstract:
Clustering (or cluster analysis) aims to
organize a collection of data items into clusters,
such that items within a cluster are more
“similar” to each other than they are to items in
the other clusters.Clustering is a typical
unsupervised learning technique for grouping
similar data points. In hard clustering, data is
divided into distinct clusters, where each data
element belongs to exactly one cluster but in fuzzy
clustering (also referred to as soft clustering),
data elements can belong to more than one
cluster, and associated with each element is a set
of membership levels. FCM can be easily trapped
into local optima and solution is sensitive to
initialization. Evolutionary algorithm can be used
for optimizing of fuzzy clustering, one of them is
Differential Evolution Algorithm. Differential
evolution (DE) algorithm is a novel evolutionary
algorithm (EA) for global optimization, where the
mutation operator is based on the distribution of
solutions in the population. The proposed system
used the differential evolution for fuzzy
clustering..Four type of UCI datasets are used for
both algorithms.
