Abstract:
Data clustering is an important area of
data mining. This is an unsupervised study where
data of similar types are put into one cluster while
data of another types are put into different
cluster.In K-means, data is divided into crisp
clusters, where each data point belongs to exactly
one cluster. In FCM, a point can belong to all
groups with different membership grades between
0 and 1.This paper presents the comparison of the
performance analysis of K-means algorithm and
Fuzzy C-means (FCM) algorithm using two
datasets from UCI in terms of entropy and
average computational time
