Name
Abstract | ||
|---|---|---|
The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable information that can improve the quality of the combination process. Besides, we propose a new similarity measure between partitions proving that it is a kernel function. A new consensus function is introduced using this similarity measure and based on the idea of finding the median partition. Related to this consensus function, some theoretical results that endorse the suitability of our methods are proven. Finally, we conduct a numerical experimentation to show the behavior of our method on several databases by making a comparison with simple clustering algorithms as well as to other cluster ensemble methods. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patcog.2010.03.001 | Pattern Recognition |
Keywords | Field | DocType |
cluster ensemble method,weighted partition consensus,simple clustering algorithm,multiple clustering result,clustering solution,cluster ensemble,new consensus function,kernel function,consensus function,new cluster ensemble method,clustering ensemble,indexation | Data mining,Similarity measure,Consensus clustering,Artificial intelligence,Cluster analysis,Ensemble learning,Relevance analysis,k-medians clustering,Pattern recognition,Partition (number theory),Machine learning,Mathematics,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
43 | 8 | Pattern Recognition |
Citations | PageRank | References |
44 | 1.27 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandro Vega-Pons | 1 | 232 | 9.79 |
| Jyrko Correa-morris | 2 | 65 | 4.17 |
| José Ruiz-Shulcloper | 3 | 626 | 46.48 |