Comparative study of k-means and rough k-means algorithms

Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. The gale of clustering is to group similar objects in one cluster and dissimilar objects in different clusters.

There are many clustering algorithms which are used to find patterns and structure in data.

This paper presents a comparative analysis between two clustering algorithms namely k-means and rough k-means.

The most frequently used clustering algorithm is the k-means which has been applied in many fields of science and technology. The k-means clustering is characterized by non-overlapping,clearly separated (crisp) clusters with bivalent membership: an object either belongs to or does not belong to a cluster.

In contrast to k-means clustering algorithms, rough clustering is inspired by intervals. It utilizes the fundamental properties of rough set theory, namely the concepts of lower and upper approximations.