Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Self-Organizing Maps
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
KANTS: Artifical Ant System for Classification
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Classification using topologically preserving spherical self-organizing maps
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
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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.