Clustering with Swarm Algorithms Compared to Emergent SOM

Abstract

Swarm-based methods are promising nature-inspired techniques. A swarm of stochastic agents performs the task of clustering high-dimensional data on a low-dimensional output space. Most swarm methods are derivatives of the Ant Colony Clustering (ACC) approach proposed by Lumer and Faieta. Compared to clustering on Emergent Self-Organizing Maps (ESOM) these methods usually perform poorly in terms of topographic mapping and cluster formation. A unifying representation for ACC methods and Emergent Self-Organizing Maps is presented in this paper. ACC terms are related to corresponding mechanisms of the SOM. This leads to insights on both algorithms. ACC can be considered to be 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 critical clustering problems.KeywordsTopographic MappingInput SampleNeighbourhood FunctionSwarm AlgorithmMinimal Path LengthThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.