Data Labeling method based on Cluster Purity using Relative Rough Entropy for Categorical Data Clustering

Clustering is an important technique in data mining. Clustering a large data set is difficult and time consuming. An approach called data labeling has been suggested for clustering large databases using sampling technique to improve efficiency of clustering. A sampled data is selected randomly for initial clustering and data points which are not sampled and unclustered are given cluster label or an outlier based on various data labeling techniques. Data labeling is an easy task in numerical domain because it is performed based on distance between a cluster and an unlabeled data point. However, in categorical domain since the distance is not defined properly between data points and between data point with cluster, then data labeling is a difficult task for categorical data. In this paper, we have proposed a method for data labeling using Relative Rough Entropy for clustering categorical data. The concept of entropy, introduced by Shannon with particular reference to information theory is a powerful mechanism for the measurement of uncertainty information. In this method, data labeling is performed by integrating entropy with rough sets. In this paper, the cluster purity is also used for outlier detection. The experimental results show that the efficiency and clustering quality of this algorithm are better than the previous algorithms.