Careful Seeding for k-Medois Clustering with Incremental k-Means plus plus Initialization
文献类型：期刊论文
| 作者 | Cheng, Difei2; Zhang, Yunfeng3; Jin, Ruinan1 |
| 刊名 | JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
 |
| 出版日期 | 2024-04-13 |
| 页码 | 21 |
| 关键词 | Clustering incremental initialization algorithm K-means plus plus |
| ISSN号 | 0218-1266 |
| DOI | 10.1142/S0218126624501846 |
| 通讯作者 | Jin, Ruinan(jinruinan@cuhk.edu.cn) |
| 英文摘要 | K-medoids clustering is a popular variant of k-means clustering and widely used in pattern recognition and machine learning. A main drawback of k-medoids clustering is that an improper initialization can cause it to get trapped in local optima. An improved k-medoids clustering algorithm, called INCKM algorithm, which is the first to apply incremental initialization to k-medoids clustering, was recently proposed to overcome this drawback. The INCKM algorithm requires the construction of a subset of candidate medoids determined by one hyperparameter for initialization, and meanwhile, it always fails when dealing with imbalanced datasets with an incorrect hyperparameter selection. In this paper, we propose a novel k-medoids clustering algorithm, called incremental k-means++ (INCKPP) algorithm, which initializes with a novel incremental manner, attempting to optimally add one new cluster center at each stage through a non-parametric and stochastic k-means++ initialization. The INCKPP algorithm overcomes the difficulty of hyperparameter selection in the INCKM algorithm, improves the clustering performance, and can deal with imbalanced datasets well. However, the INCKPP algorithm is not computationally efficient enough. To deal with this, we further propose an improved INCKPP algorithm, called INCKPPsample algorithm which improves the clustering efficiency while maintaining the clustering performance of the INCKPP algorithm. Extensive results from experiments on both synthetic and real-world datasets, including imbalanced datasets, illustrate that the proposed algorithms outperforms than the other compared algorithms. |
| WOS关键词 | MEANS ALGORITHM |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001202128500002 |
| 出版者 | WORLD SCIENTIFIC PUBL CO PTE LTD |
| 源URL | [http://ir.ia.ac.cn/handle/173211/57032]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 通讯作者 | Jin, Ruinan |
| 作者单位 | 1.Chinese Univ Hong Kong, Sch Data Sci, Shenzhen 518172, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cheng, Difei,Zhang, Yunfeng,Jin, Ruinan. Careful Seeding for k-Medois Clustering with Incremental k-Means plus plus Initialization[J]. JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS,2024:21. |
| APA | Cheng, Difei,Zhang, Yunfeng,&Jin, Ruinan.(2024).Careful Seeding for k-Medois Clustering with Incremental k-Means plus plus Initialization.JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS,21. |
| MLA | Cheng, Difei,et al."Careful Seeding for k-Medois Clustering with Incremental k-Means plus plus Initialization".JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS (2024):21. |
入库方式： OAI收割
来源：自动化研究所
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