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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.
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hal-02115652 , version 1 (30-04-2019)

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  • HAL Id : hal-02115652 , version 1

Cite

Nesrine Masmoudi, Hanane Azzag, Mustapha Lebbah, Cyrille Bertelle, Maher Ben Jemaa. Clustering of Binary Data Sets Using Artificial Ants Algorithm. Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science, vol 9489, pp.716-723, 2015. ⟨hal-02115652⟩
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