Abstract:
This paper presents an algorithm which extends the
fuzzy k-means algorithm for clustering categorical objects. In
this algorithm, use a simple matching dissimilarity measure to
deal with categorical objects and uses a frequency-based
method to update the representative objects in the clustering
process to minimize the clustering cost function. The fuzzy krepresentative
algorithm is that it not only partition objects into
clusters but also shows how confident an object is assigned to a
cluster. The confidence is determined by the dissimilarity
measures of an object to all cluster representatives. This paper
is provided a method to find the fuzzy cluster representatives
when the simple matching dissimilarity measure is used for
categorical objects.
