Name
Abstract | ||
|---|---|---|
Two basic issues for data analysis and kernel-machines design are approached in this paper: determining the number of partitions of a clustering task and the parameters of kernels. A distance metric is presented to determine the similarity between kernels and FCM proximity matrices. It is shown that this measure is maximized, as a function of kernel and FCM parameters, when there is coherence with embedded structural information. We show that the alignment function can be maximized according FCM and kernel parameters. The results presented shed some light on the general problem of setting up the number of partitions in a clustering task and in the proper setting of kernel parameters according to structural information. |
Year | Venue | Keywords |
|---|---|---|
2009 | PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE | Affinity matrix, Clustering, Fuzzy C-Means (FCM), Kernel matrix, Reordering, Sorting |
Field | DocType | Citations |
Radial basis function kernel,Pattern recognition,Kernel embedding of distributions,Metric (mathematics),Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Cluster analysis,Variable kernel density estimation,Mathematics,Kernel (statistics) | Conference | 1 |
PageRank | References | Authors |
0.36 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Queiroz | 1 | 1 | 0.36 |
| Antonio Braga | 2 | 1 | 0.36 |
| W. Pedrycz | 3 | 13966 | 1005.85 |