  Clustering is spotting pattern in a group of objects and resultantly grouping
the similar objects together. Objects have attributes which are not always
numerical, sometimes attributes have domain or categories to which they could
belong to. Such data is called categorical data. To group categorical data many
clustering algorithms are used, among which k- modes algorithm has so far given
the most significant results. Nevertheless, there is still a lot which could be
improved. Algorithms like k-means, fuzzy-c-means or hierarchical have given far
better accuracies with numerical data. In this paper, we have proposed a novel
distance metric, similarity-based distance (SBD) to find the distance between
objects of categorical data. Experiments have shown that our proposed distance
(SBD), when used with the SBC (space structure based clustering) type algorithm
significantly outperforms the existing algorithms like k-modes or other SBC
type algorithms when used on categorical datasets.
