Name
Abstract | ||
|---|---|---|
In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1142/S021821300700362X | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS |
Keywords | Field | DocType |
clustering, genetic algorithms, local iteration algorithm | CURE data clustering algorithm,Computer science,FLAME clustering,Artificial intelligence,Cluster analysis,k-medians clustering,Canopy clustering algorithm,Correlation clustering,Pattern recognition,Determining the number of clusters in a data set,Algorithm,Machine learning,DBSCAN | Journal |
Volume | Issue | ISSN |
16 | 6 | 0218-2130 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongguo Liu | 1 | 156 | 18.45 |
| Xiaorong Pu | 2 | 85 | 11.17 |
| Yi-Dong Shen | 3 | 727 | 56.57 |
| Zhang Yi | 4 | 1765 | 194.41 |
| Xiaofeng Liao | 5 | 3657 | 326.61 |