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A novel ant-based clustering approach for document clustering

He, Yulan; Hui, Siu Cheung and Sim, Yongxiang (2006). A novel ant-based clustering approach for document clustering. In: Third Asia Information Retrieval Symposium (AIRS 2006), 16-18 Oct 2006, Singapore, Springer, pp. 537–544.

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URL: http://www.springerlink.com/content/p217p3636345n4...
DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1007/11880592_43
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Abstract

Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant Colony Optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper proposes a novel document clustering approach based on ACO. Unlike other ACO-based clustering approaches which are based on the same scenario that ants move around in a 2D grid and carry or drop objects to perform categorization. Our proposed ant-based clustering approach does not rely on a 2D grid structure. In addition, it can also generate optimal number of clusters without incorporating any other algorithms such as K-means or AHC. Experimental results on the subsets of 20 Newsgroup data show that the ant-based clustering approach outperforms the classical document clustering methods such as K-means and Agglomerate Hierarchical Clustering. It also achieves better results than those obtained using the Artificial Immune Network algorithm when tested in the same datasets.

Item Type: Conference Item
Copyright Holders: 2006 Springer-Verlag
ISSN: 0302-9743
Extra Information: Information Retrieval Technology
Third Asia Information Retrieval Symposium, AIRS 2006
Singapore, October 16-18, 2006
Proceedings
Edited by Hwee Tou Ng, Mun-Kew Leong, Min-Yen Kan, Donghong Ji
ISBN-13 978-3-540-45780-0
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 23369
Depositing User: Kay Dave
Date Deposited: 30 Mar 2011 08:40
Last Modified: 26 Oct 2012 04:31
URI: http://oro.open.ac.uk/id/eprint/23369
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