Entropy Based Mean Clustering: An Enhanced Clustering Approach

Many applications of clustering require the use of normalized data, such as text data or mass spectra mining data. The K –Means Clustering Algorithm is one of the most widely used clustering algorithm which works on greedy approach. Major problems with the traditional K mean clustering is generation of empty clusters and more computations required to make the group of clusters. To overcome this problem we proposed an Algorithm namely Entropy Based Means Clustering Algorithm. The proposed Algorithm produces normalized cluster centers, hence highly useful for text data or massive data. The proposed algorithm shows better performance when compared with traditional K Mean Clustering Algorithm in mining data in terms of reducing time, seed predications and avoiding Empty Clusters.