Data Labeling method based on Rough Entropy for categorical data clustering

Clustering is one of the most important method in data mining. Clustering a huge data set is difficult and time taking process. In this scenario a new method proposed that is based on Rough Entropy for improving efficiency of clustering and labeling the unlabeled data points in clusters. Data Labeling is a simple process in numerical domain but not in categorical domain. Why because distance is a major parameter in numerical whereas not in categorical attributes. So, In this paper proposed a new method for data labeling using Rough Entropy for clustering categorical data attributes. This method is mainly divided into two phases. Phase-1 is aimed to find the partition with respect to attributes and phase-II is to find the Rough Entropy to know the node importance for data labeling.