A GRASP algorithm for clustering

被引：0

作者：

Cano, JR ^{[1
]}

Cordón, O

Herrera, F

Sánchez, L

机构：

[1] Univ Huelva, Dept Software Engn, La Rabida 21071, Huelva, Spain

[2] Univ Granada, Dept Comp Sci & AI, E-18071 Granada, Spain

[3] Univ Oviedo, Dept Comp Sci, Oviedo, Spain

来源：

ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2002, PROCEEDINGS | 2002年 / 2527卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 for getting initial solutions and K-Means algorithm as a local search algorithm. We compare it with some typical initialization methods: Random, Forgy, Macqueen and Kaufman. The new approach obtains high quality solutions for the benchmark problems.

引用

页码：214 / 223

页数：10