Clustering of binary data sets using artificial ants algorithm

Abstract

As an important technique for data mining, clustering often consists in forming a set of groups according to a similarity measure such as hamming distance. In this paper, we present a new bio-inspired model based on artificial ants over a dynamical graph of clusters using colonial odors and pheromone-based reinforcement process. Results analysis are provided and based on the impact of parameter values on purity index which is a measure of clustering quality. Dynamic evolution of cluster graph topologies are presented on two databases from Machine Learning Repository.