Name
Abstract | ||
|---|---|---|
Clustering is a popular data analysis and data mining technique. In this paper, an improved ant colony clustering algorithm is presented to optimally partition N objects into K clusters and a comparative study has been made to prove its high performance using four evaluation measures. This algorithm has been tested on several synthetic datasets compared with the proposed ant colony based clustering algorithm called ACA. The experimental data reveals very encouraging results in terms of the quality of clustering. © 2012 IEEE. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICNC.2012.6234748 | ICNC |
Keywords | Field | DocType |
aca,aco,clustering,icpaca,comparative study,algorithm design and analysis,shape,data analysis,classification algorithms,clustering algorithms,ant colony,data mining,indexes | Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Single-linkage clustering,k-medians clustering,Canopy clustering algorithm,Pattern recognition,Affinity propagation,Correlation clustering,Determining the number of clusters in a data set,Machine learning | Conference |
Volume | Issue | Citations |
null | null | 1 |
PageRank | References | Authors |
0.41 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changsheng Zhang | 1 | 7 | 3.59 |
| Mengli Zhu | 2 | 1 | 0.41 |
| Bin Zhang | 3 | 213 | 41.40 |