Entropy Based Clustering of Data Streams with Mixed Numeric and Categorical Values

In is paper, a novel algorithm for clustering data streams with mixed numeric and categorical attributes (CNC-Stream)is proposed. A new similarity measure based on entropy determining the similarity between the objects(data points in the stream or the micro- clusters in memory) is also presented here, which makes CNC-Stream work, the experiments conducted on the real data sets and synthetic data sets show that the proposed method is of high quality.