An Entropy Regularization <em>k</em>-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering
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 t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/7/683 |
| _version_ | 1828283084281217024 |
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| author | Liyan Xiong Cheng Wang Xiaohui Huang Hui Zeng |
| author_facet | Liyan Xiong Cheng Wang Xiaohui Huang Hui Zeng |
| author_sort | Liyan Xiong |
| collection | DOAJ |
| description | 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 <i>k</i>-means-type clustering algorithms in most cases. |
| first_indexed | 2024-04-13T08:39:39Z |
| format | Article |
| id | doaj.art-1fa85fd20c7d4faa80cf093fd0139dd5 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T08:39:39Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-1fa85fd20c7d4faa80cf093fd0139dd52022-12-22T02:53:56ZengMDPI AGEntropy1099-43002019-07-0121768310.3390/e21070683e21070683An Entropy Regularization <em>k</em>-Means Algorithm with a New Measure of between-Cluster Distance in Subspace ClusteringLiyan Xiong0Cheng Wang1Xiaohui Huang2Hui Zeng3School of Information Engineering Department, East China Jiaotong University, R.d 808, East Shuanggang Avenue, Nanchang 330013, ChinaSchool of Information Engineering Department, East China Jiaotong University, R.d 808, East Shuanggang Avenue, Nanchang 330013, ChinaSchool of Information Engineering Department, East China Jiaotong University, R.d 808, East Shuanggang Avenue, Nanchang 330013, ChinaSchool of Information Engineering Department, East China Jiaotong University, R.d 808, East Shuanggang Avenue, Nanchang 330013, ChinaAlthough 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 <i>k</i>-means-type clustering algorithms in most cases.https://www.mdpi.com/1099-4300/21/7/683k-meansbetween-cluster informationentropy regularizationdata mining |
| spellingShingle | Liyan Xiong Cheng Wang Xiaohui Huang Hui Zeng An Entropy Regularization <em>k</em>-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering Entropy k-means between-cluster information entropy regularization data mining |
| title | An Entropy Regularization <em>k</em>-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering |
| title_full | An Entropy Regularization <em>k</em>-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering |
| title_fullStr | An Entropy Regularization <em>k</em>-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering |
| title_full_unstemmed | An Entropy Regularization <em>k</em>-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering |
| title_short | An Entropy Regularization <em>k</em>-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering |
| title_sort | entropy regularization em k em means algorithm with a new measure of between cluster distance in subspace clustering |
| topic | k-means between-cluster information entropy regularization data mining |
| url | https://www.mdpi.com/1099-4300/21/7/683 |
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