Name
Abstract | ||
|---|---|---|
The self-organization behavior exhibited by ants may be modeled to solve real world clustering problems. The general idea of artificial ants walking around in search space to pick up, or drop an item based upon some probability measure has been examined to cluster a large number of World Wide Web (WWW) documents. However, this idea is extended with the direct application of template matching with a Gaussian Probability Surface (GPS) to constrain the formation of the clusters in pre-defined areas of workspace with these multi-agents in this paper. Some comparisons between the clustering performance of supervised ants using GPS against the typical ants clustering algorithm are shown. Their performance are evaluated on the same dataset consisting of a collection of multi-class web documents. Finally, the paper concludes with some recommendations for further investigation. |
Year | Venue | Keywords |
|---|---|---|
2005 | INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS | Swarm intelligence, Ant colony optimization, Data clustering, Document clustering |
Field | DocType | Volume |
Template matching,Data mining,Categorization,Workspace,Computer science,Probability measure,Gaussian,Artificial intelligence,Global Positioning System,Cluster analysis,Artificial Ants,Machine learning | Journal | 29 |
Issue | ISSN | Citations |
2 | 0350-5596 | 5 |
PageRank | References | Authors |
0.60 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siok Lan Ong | 1 | 5 | 0.94 |
| Weng-kin Lai | 2 | 33 | 5.49 |
| Tracy S. Y. Tai | 3 | 24 | 3.20 |
| Kok Meng Hoe | 4 | 33 | 4.22 |
| Choo Hau Ooi | 5 | 5 | 0.94 |