An Approach for Data Labelling and Concept Drift Detection Based on Entropy Model in Rough Sets for Clustering Categorical Data

Abstract

Clustering is an important technique in data mining. Clustering a large data set is difficult and time consuming. An approach called data labelling 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 labelling techniques. Data labelling is an easy task in numerical domain because it is performed based on distance between a cluster and an unlabelled data point. However, in categorical domain since the distance is not defined properly between data points and data points with cluster, then data labelling is a difficult task for categorical data. This paper proposes a method for data labelling using entropy model in rough sets for 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 labelling is performed by integrating entropy with rough sets. This method is also applied to drift detection to establish if concept drift occurred or not when clustering categorical data. The cluster purity is also discussed using Rough Entropy for data labelling and for outlier detection. The experimental results show that the efficiency and clustering quality of this algorithm are better than the previous algorithms.