An aggregated clustering approach using multi-ant colonies algorithms

✍ Scribed by Yan Yang; Mohamed S. Kamel

• Publisher: Elsevier Science
• Year: 2006
• Tongue: English
• Weight: 504 KB
• Volume: 39
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2006.02.012

✦ Synopsis

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.