Clustering via Ant Colonies: Parameter Analysis and Improvement of the Algorithm

TL;DR

This paper introduces an ant colony optimization algorithm for clustering that combines pheromone-based solution construction with K-means refinement, demonstrating promising results on real datasets after parameter tuning.

Contribution

It proposes a novel ant colony clustering method with parameter analysis and improvements using K-means, validated through simulations and real data experiments.

Findings

Method effectively minimizes intra-variance in clustering.

Parameter tuning improves clustering performance.

Encouraging results on benchmark datasets.

Abstract

An ant colony optimization approach for partitioning a set of objects is proposed. In order to minimize the intra-variance, or within sum-of-squares, of the partitioned classes, we construct ant-like solutions by a constructive approach that selects objects to be put in a class with a probability that depends on the distance between the object and the centroid of the class (visibility) and the pheromone trail; the latter depends on the class memberships that have been defined along the iterations. The procedure is improved with the application of K-means algorithm in some iterations of the ant colony method. We performed a simulation study in order to evaluate the method with a Monte Carlo experiment that controls some sensitive parameters of the clustering problem. After some tuning of the parameters, the method has also been applied to some benchmark real-data sets. Encouraging results were obtained in nearly all cases.