Ant-Based Clustering and Topographic Mapping

Abstract

Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous investigation of its capabilities. We describe an improved version, called ATTA, incorporating adaptive, heterogeneous ants, a time-dependent transporting activity, and a method (for clustering applications) that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date: we compare its performance with standard techniques for clustering and topographic mapping using a set of analytical evaluation functions and a range of synthetic and real data collections. Our results demonstrate the ability of ant-based clustering and sorting to automatically identify the number of clusters inherent in a data collection, and to produce high quality solutions; indeed, we show that it is particularly robust for clusters of differing sizes and for overlapping clusters. The results obtained for topographic mapping are, however, disappointing. We provide evidence that the solutions generated by the ant algorithm are barely topolog y-preserving, and we explain in detail why results have -in spite of this -been misinterpreted (much more positively) in previous research.

Modeling acquaintance networks based on balance theory

“…This paradigm was successfully used in combinatorial and continuous optimization (Dorigo et al, 2000;1996;Korošec, 2006;Korošec et al, 2012) and network analysis (Handl et al, 2006;Martens et al, 2007).…”

A Multiple Pheromone Ant Clustering Algorithm

“…Handl et al introduce the Adaptive Time Dependent Transporter Ants (ATTA), incorporating adaptive heterogeneous ants, a time-dependent transporting activity and a method that transforms the spatial embedding produced by the algorithm into an explicit partitioning [Handl et al, 2006]. They demonstrate that their proposed modifications yield significant improvements in terms of quality and speed of the solution generated.…”

Ant-based Clustering Algorithms: A Brief Survey

“…Handl, J. Knowles and M. Dorigo [37] described an improved version of the heuristic, called Adaptive Time Dependent Transporter Ants (ATTA), incorporating adaptive heterogeneous ants, a time-dependent transporting activity, and a method that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date.…”

“…Gillner [28] investigated the performance of ACLUSTER [63] and ATTA [37], under the measures and datasets proposed by Handl et al [37]. Based on performance results of both algorithms gathered from numerous runs, the results indicate weaknesses in the design of ACLUSTER while ATTA is well capable of clustering tasks.…”