Name
Title | ||
|---|---|---|
A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure |
Abstract | ||
|---|---|---|
We present a new approach for Cluster Analysis based on a Greedy Randomized Adaptive Search Procedure (GRASP), with the objective of overcoming the convergence to a local solution. It uses a probabilistic greedy Kaufman initialization to get initial solutions and K-Means as a local search algorithm. The approach is a new initialization one for K-Means. Hence, we compare it with some typical initialization methods: Random, Forgy, Macqueen and Kaufman. Our empirical results suggest that the hybrid GRASP - K-Means with probabilistic greedy Kaufman initialization performs better than the other methods with improved results. The new approach obtains high quality solutions for eight benchmark problems. |
Year | Venue | Keywords |
|---|---|---|
2002 | Journal of Intelligent and Fuzzy Systems | k-means,hybrid GRASP,probabilistic greedy Kaufman initialization,local solution,k-means.,new initialization,local search algorithm,Greedy Randomized Adaptive Search,local search procedure,new approach,clustering problem,greedy randomized adaptive search,initialization process,greedy randomized adaptive search procedure,typical initialization method,benchmark problem,Cluster Analysis,clustering |
Field | DocType | Volume |
k-means clustering,Mathematical optimization,GRASP,Artificial intelligence,Probabilistic logic,Local search (optimization),Initialization,Cluster analysis,Greedy randomized adaptive search procedure,Best-first search,Mathematics,Machine learning | Journal | 12 |
Issue | ISSN | Citations |
3 | 1064-1246 | 11 |
PageRank | References | Authors |
0.72 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. R. Cano | 1 | 133 | 5.66 |
| O. Cordón | 2 | 1380 | 66.74 |
| Francisco Herrera | 3 | 27391 | 1168.49 |
| Sanchez, L. | 4 | 377 | 23.74 |