Xiong Liyan, Wang Cheng, Huang Xiaohui, Zeng Hui
School of Information Engineering Department, East China Jiaotong University, R.d 808, East Shuanggang Avenue, Nanchang 330013, China.
Entropy (Basel). 2019 Jul 12;21(7):683. doi: 10.3390/e21070683.
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 -means-type clustering algorithms in most cases.
尽管在大多数聚类方法中通常会使用簇内信息，但在某些情况下，其他重要信息（如簇间信息）却很少被考虑。因此，在本研究中，我们提出了一种新的子空间中簇间距离的新颖度量方法，即最大化一个簇的中心与不属于该簇的点之间的距离。基于此想法，我们首先在本文中设计了一个将簇间距离和熵正则化相结合的优化目标函数。然后，通过理论分析给出更新规则。接下来，研究了我们提出的算法的性质，并使用两个合成数据集和七个真实数据集进行了实验性能评估。最后，实验研究表明，在大多数情况下，所提出的算法（ERKM）的结果优于大多数现有的最先进的均值型聚类算法。
