Authors:
Mao Lin Huang
1
;
Jun Lai
2
and
Ben Soh
2
Affiliations:
1
University of Technology, Australia
;
2
La Trobe University Bundoora, Australia
Keyword(s):
clustering, information filtering, information retrieval, search engine, World Wide Web.
Related
Ontology
Subjects/Areas/Topics:
Agent-Based Information Systems
;
Business and Social Applications
;
Communication and Software Infrastructure
;
Distributed Intelligent Agents
;
e-Business
;
e-Commerce and e-Business: B2B and B2C
;
e-Marketing and Consumer Behaviour
;
Enterprise Information Systems
;
Enterprise Software Technologies
;
Global Communication Information Systems and Services
;
Information and Systems Security
;
Mobile and Pervasive Computing
;
Software Engineering
;
Telecommunications
;
Web and Mobile Business Systems and Services
;
Web Technologies and Web Services
Abstract:
As the use of the web grows globally and exponentially, it becomes increasingly harder for users to find the information they want. Therefore, there is a need for good information filtering mechanisms. This paper presents a new, efficient information filtering method using word clusters. Traditional filtering methods only consider the relevance values of document. As a result, these conventional methods fail to consider the efficiency of document retrieval, which is also crucial. Our algorithm using offline computation attempts to cluster similar documents based on words shared by documents to produce clusters, so that the efficiency of information filtering and retrieval can be improved.