摘要
This paper presents a new online clustering algorithm called SAFN which is used to learn continuously evolving clusters from non-stationary data. The SAFN uses a fast adaptive learning procedure to take into account variations over time. In non-stationary and multi-class environment, the SAFN learning procedure consists of five main stages: creation, adaptation, mergence, split and elimination. Experiments are carried out in three kinds of datasets to illustrate the performance of the SAFN algorithm for online clustering. Compared with SAKM algorithm, SAFN algorithm shows better performance in accuracy of clustering and multi-class high-dimension data.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 |
| 页 | 1104-1108 |
| 页数 | 5 |
| DOI | |
| 出版状态 | 已出版 - 2011 |
| 活动 | 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11 - Shanghai, 中国 期限: 26 7月 2011 → 28 7月 2011 |
出版系列
| 姓名 | Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 |
|---|---|
| 卷 | 2 |
会议
| 会议 | 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11 |
|---|---|
| 国家/地区 | 中国 |
| 市 | Shanghai |
| 时期 | 26/07/11 → 28/07/11 |
指纹
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Li, K., Yao, F., & Liu, R. (2011). An online clustering algorithm. 在 Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 (页码 1104-1108). 文章 6019762 (Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011; 卷 2). https://doi.org/10.1109/FSKD.2011.6019762