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Comparing an Ant-Based Clustering Algorithm with Self-Organizing Maps and K-means


Clodis Boscarioli, Rosangela Villwock, Bruno Eduardo Soares


Vol. 12  No. 9  pp. 49-54


The data analysis involves the performance of different tasks, which can be performed by many different techniques and strategies. The data clustering task, an unsupervised pattern recognition process, is the task of assigning a set of objects into groups called clusters so that the objects in the same cluster are more similar to each other than to those in other clusters. This paper describes three different approaches to Data Clustering using the artificial neural network Self-Organizing Maps, K-means and an Ant-based Algorithm proposal, and the experimental results are discussed comparing their performance.


Ant Colony, Self-Organizing Maps, Experimental Evaluation, Data Clustering