How to use ants for data stream clustering

We present in this paper a new bio-inspired algorithm that dynamically creates groups of data. This algorithm is based on the concept of artificial ants that move together in a complex manner with simple localization rules. Each ant represents one datum in the algorithm. The moves of ants aim at cre...

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Bibliographic Details
Main Authors: Masmoudi, Nesrine, Azzag, Hanane, Lebbah, Mustapha, Bertelle, Cyrille, Ben Jemaa, Maher
Format: Conference Proceeding
Language:English
Subjects:
Ants behavior
Artificial ants model
Clustering
Clustering algorithms
Heuristic algorithms
Indexes
Niobium
Particle swarm optimization
Partitioning algorithms
Swarm intelligence
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Summary:We present in this paper a new bio-inspired algorithm that dynamically creates groups of data. This algorithm is based on the concept of artificial ants that move together in a complex manner with simple localization rules. Each ant represents one datum in the algorithm. The moves of ants aim at creating homogeneous groups of data that evolve together in a graph environment. We also suggest an extension to this algorithm to treat data streaming. The extended algorithm has been tested on real-world data. Our algorithms yielded competitive results as compared to K-means and Ascending Hierarchical Clustering (AHC), two well known methods.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2015.7256953