An aggregated clustering approach using multi-ant colonies algorithms

Authors:
Yan Yang;Mohamed S. Kamel
Affiliations:
School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China;Pattern Analysis and Machine Intelligence Lab, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
Venue:
Pattern Recognition
Year:
2006

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Abstract

This paper presents a multi-ant colonies approach for clustering data that consists of some parallel and independent ant colonies and a queen ant agent. Each ant colony process takes different types of ants moving speed and different versions of the probability conversion function to generate various clustering results with an ant-based clustering algorithm. These results are sent to the queen ant agent and combined by a hypergraph model to calculate a new similarity matrix. The new similarity matrix is returned back to each ant colony process to re-cluster the data using the new information. Experimental evaluation shows that the average performance of the aggregated multi-ant colonies algorithms outperforms that of the single ant-based clustering algorithm and the popular K-means algorithm. The result also shows that the lowest outliers strategy for selecting the current data set has the best performance quality.