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An Entropy Regularization k-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering

School of Information Engineering Department, East China Jiaotong University, R.d 808, East Shuanggang Avenue, Nanchang 330013, China
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Entropy 2019, 21(7), 683; https://doi.org/10.3390/e21070683
Received: 15 April 2019 / Revised: 6 July 2019 / Accepted: 10 July 2019 / Published: 12 July 2019
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Abstract

Although within-cluster information is commonly used in most clustering approaches, other important information such as between-cluster information is rarely considered in some cases. Hence, in this study, we propose a new novel measure of between-cluster distance in subspace, which is to maximize the distance between the center of a cluster and the points that do not belong to this cluster. Based on this idea, we firstly design an optimization objective function integrating the between-cluster distance and entropy regularization in this paper. Then, updating rules are given by theoretical analysis. In the following, the properties of our proposed algorithm are investigated, and the performance is evaluated experimentally using two synthetic and seven real-life datasets. Finally, the experimental studies demonstrate that the results of the proposed algorithm (ERKM) outperform most existing state-of-the-art k-means-type clustering algorithms in most cases. View Full-Text
Keywords: k-means; between-cluster information; entropy regularization; data mining k-means; between-cluster information; entropy regularization; data mining
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Xiong, L.; Wang, C.; Huang, X.; Zeng, H. An Entropy Regularization k-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering. Entropy 2019, 21, 683.

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