Name
Abstract | ||
|---|---|---|
Cluster ensemble is a good alternative to face the problem of data clustering. Some studies based on mathematical models have shown that cluster ensemble methods lead to an effective improvement of the results of the standard clustering algorithms. In this paper, we focus on this problem, proposing a new approach to solve it, by adding a new step into the usual cluster ensemble methodology. Representing partitions by graphs and a new kernel function to measure the similarity between partitions are other proposals for this work. Experiments with synthetic and real databases show the suitability and effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-85920-8_24 | CIARP |
Keywords | Field | DocType |
cluster ensemble method,standard clustering algorithm,kernel consensus function,good alternative,new step,cluster ensemble,representing partition,usual cluster ensemble methodology,new kernel function,effective improvement,new approach,weighted cluster ensemble,kernel function,data clustering,mathematical model | Kernel (linear algebra),Graph kernel,Data mining,Pattern recognition,Computer science,Consensus function,Artificial intelligence,Mean-shift,Cluster analysis,Mathematical model,Ensemble learning,Kernel (statistics) | Conference |
Volume | ISSN | Citations |
5197 | 0302-9743 | 14 |
PageRank | References | Authors |
0.78 | 11 | 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 |