A Case Study of Clustering Algorithms for Categorical Data sets

The data clustering, an unsupervised pattern recognition process is the task of assigning a set of objects into groups called clusters so that the objects in the same cluster are more similar to each other than to those in other clusters. Most traditional clustering algorithms are limited to handling numerical data. However, these cannot be directly applied for clustering of nominal data, where domain values are discrete and have no ordering. In this paper various categorical data clustering algorithms are going to be addressed in detail. A detailed survey on existing algorithms will be made and the scalability of some of the existing algorithms will be examined