A Hierarchical Clustering for Categorical Data Based on Holo-Entropy

Abstract

High dimensional data clustering is a difficult task in clustering analysis. Subspace clustering is an effective approach. The principle of subspace clustering is to maximize the retention of the original data information while searching for the minimal size of subspace for cluster representation. Based on information entropy and Holo-entropy, we propose an adaptive high dimensional weighted subspace clustering algorithm. The algorithm employs information entropy to extract the feature subspace, uses class compactness which binding Holo-entropy with weight in subspace for sub-clusters merging instead of the traditional similarity measurement method, and it selects the most compacted two sub-clusters to merge to achieve the maximum degree clustering effect. The algorithm is tested on nine UCI dataset, and compared with other algorithms. Our algorithm is better in both efficiency and accuracy than the other existing algorithms and has high reproducibility.