Efficiency based categorical data clustering

Clustering is a useful and efficient task in data mining which is used in database related applications. Existing work on clustering focused on only categorical data which is based on attribute values for grouping similar kind of data. This paper is based on clustering the continuous and categorical data set in efficient manner. The goal is to use integrated clustering approach based on high dimensional categorical data that works well for data with mixed continuous and categorical features. The experimental results of the proposed method on several data sets suggests that the link based cluster ensemble algorithm when integrate with k-means algorithm to produce final results. The scope of this proposed work is used to provide the accurate and efficient results, whenever the user wants to access the data from the database.