Paper Info
Title
Investigations into the Use of Supervised Multi-agents for Web Documents Categorization
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 [1]. In this paper, we present a preliminary investigation on the direct application of a Gaussian Probability Surface (GPS) to constrain the formation of the clusters in pre-defined areas of workspace with these multiagents. We include a comparison between the clustering performances of supervised ants using GPS against the typical ants clustering algorithm. The performance of both supervised and unsupervised systems will be 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
DOI
Venue
2004
10.1007/978-3-540-30499-9_171
Lecture Notes in Computer Science
Keywords
Field
DocType
self organization,probability measure,world wide web,search space
Categorization,Data mining,Workspace,Document management system,Computer science,Swarm intelligence,Probability measure,Global Positioning System,Artificial intelligence,Cluster analysis,Machine learning,The Internet
Conference
Volume
ISSN
Citations 
3316
0302-9743
0
PageRank 
References 
Authors
0.34
3
5
Name
Order
Citations
PageRank
Siok Lan Ong150.94
Weng-kin Lai2335.49
Tracy S. Y. Tai3243.20
Choo Hau Ooi450.94
Kok Meng Hoe5334.22