Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007 |
| Pages | 365-368 |
| Number of pages | 4 |
| Publication status | Published - 2007 |
| Event | 2007 International Conference on Artificial Intelligence, ICAI 2007 - Las Vegas, NV, United States Duration: 25 Jun 2007 → 28 Jun 2007 |
Publication series
| Name | Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007 |
|---|---|
| Volume | 1 |
Conference
| Conference | 2007 International Conference on Artificial Intelligence, ICAI 2007 |
|---|---|
| Country/Territory | United States |
| City | Las Vegas, NV |
| Period | 25/06/07 → 28/06/07 |
Keywords
- Cluster number
- Eigengap
- Kernel
- Spectral clustering
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Li, K., & Liu, Y. (2007). A novel spectral clustering algorithm. In Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007 (pp. 365-368). (Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007; Vol. 1).