Strengths and Weaknesses of Ant Colony Clustering

Abstract

Ant colony clustering (ACC) is a promising nature-inspired technique where stochastic agents perform the task of clustering high-dimensional data on a low-dimensional output space. Most ACC methods are derivatives of the approach proposed by Lumer and Faieta. These methods usually perform poorly in terms of topographic mapping and cluster formation. In particular when compared to clustering on Emergent Self-Organizing Maps (ESOM). In order to address this issue, a unifying representation for ACC methods and Emergent Self-Organizing Maps is derived in a brief yet formal manner. ACC terms are related to corresponding mechanisms of the Self-Organizing Map. This leads to insights on both algorithms. ACC are considered as first-degree relatives of the ESOM. This explains benefits and shortcomings of ACC and ESOM. Furthermore, the proposed unification allows to judge whether modifications improve an algorithm’s clustering abilities or not. This is demonstrated using a set of cardinal clustering problems.