Title :
How to use ants for data stream clustering
Author :
Masmoudi, Nesrine ; Azzag, Hanane ; Lebbah, Mustapha ; Bertelle, Cyrille ; Ben Jemaa, Maher
Author_Institution :
LITIS University of Havre, France-76600
Abstract :
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.
Keywords :
Clustering algorithms; Heuristic algorithms; Indexes; Niobium; Particle swarm optimization; Partitioning algorithms; Ants behavior; Artificial ants model; Clustering; Swarm intelligence;
Conference_Titel :
Evolutionary Computation (CEC), 2015 IEEE Congress on
Conference_Location :
Sendai, Japan
DOI :
10.1109/CEC.2015.7256953