摘要
In the spectral clustering algorithm, determination of cluster number is a difficult problem. Here an autonomous spectral clustering algorithm is proposed. Eigengap is used to discover the clustering stability and decide automatically the cluster number, which is proved theoretically the rationality of cluster number. A kernel based fuzzy c-means is introduced to spectral clustering algorithm. Finally our algorithm compares with c-means, Ng et.al's algorithm and Francesco et.al's algorithm in the UCI data sets(IRIS data and Wisconsin database). The experiments show our algorithm may get better results.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007 |
| 页 | 365-368 |
| 页数 | 4 |
| 出版状态 | 已出版 - 2007 |
| 活动 | 2007 International Conference on Artificial Intelligence, ICAI 2007 - Las Vegas, NV, 美国 期限: 25 6月 2007 → 28 6月 2007 |
出版系列
| 姓名 | Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007 |
|---|---|
| 卷 | 1 |
会议
| 会议 | 2007 International Conference on Artificial Intelligence, ICAI 2007 |
|---|---|
| 国家/地区 | 美国 |
| 市 | Las Vegas, NV |
| 时期 | 25/06/07 → 28/06/07 |
指纹
探究 'A novel spectral clustering algorithm' 的科研主题。它们共同构成独一无二的指纹。引用此
Li, K., & Liu, Y. (2007). A novel spectral clustering algorithm. 在 Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007 (页码 365-368). (Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007; 卷 1).