The Open UniversitySkip to content

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: Information Retrieval Technology, Lecture Notes in Computer Science, Springer, pp. 537–544.

Full text available as:
Full text not publicly available (Version of Record)
Due to publisher licensing restrictions, this file is not available for public download
DOI (Digital Object Identifier) Link:
Google Scholar: Look up in Google Scholar


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 or Workshop 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
Edited by Hwee Tou Ng, Mun-Kew Leong, Min-Yen Kan, Donghong Ji
ISBN-13 978-3-540-45780-0
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 23369
Depositing User: Kay Dave
Date Deposited: 30 Mar 2011 08:40
Last Modified: 11 Dec 2018 19:06
Share this page:


Altmetrics from Altmetric

Citations from Dimensions

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU