Farthest-Point Heuristic based Initialization Methods for K-Modes
  Clustering
  The k-modes algorithm has become a popular technique in solving categorical
data clustering problems in different application domains. However, the
algorithm requires random selection of initial points for the clusters.
Different initial points often lead to considerable distinct clustering
results. In this paper we present an experimental study on applying a
farthest-point heuristic based initialization method to k-modes clustering to
improve its performance. Experiments show that new initialization method leads
to better clustering accuracy than random selection initialization method for
k-modes clustering.
