Topic discovery from document using ant-based clustering combination

Authors:
Yan Yang;Mohamed Kamel;Fan Jin
Affiliations:
Yan Yang and Fan Jin, School of Computer and Communication Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China;Mohamed Kamel, Pattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada;Yan Yang and Fan Jin, School of Computer and Communication Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China
Venue:
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Year:
2005

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Abstract

This paper presents a topic discovery approach based on multi-ant colonies clustering combination. The algorithm consists of three parts. First, each document is represented as a vector of features in a vector space model. Then a hypergraph model is used to combine the clusterings produced by three kinds of ant-based algorithms with different moving speed. Finally, the topic of each cluster is extracted by re-computing the term weights. Test results show that the number of topics can be adaptively determined and clustering combination can improve the system performance.