Title :
Incremental clustering of data stream using real ants behavior
Author :
Masmoudi, N. ; Azzag, Hanane ; Lebbah, Mustapha ; Bertelle, Cyrille
Author_Institution :
LITIS, Univ. of Havre, Le Havre, France
fDate :
July 30 2014-Aug. 1 2014
Abstract :
We present in this paper a new biomimetic method nammed CL-AntInc for data incremental clustering. This algorithm uses the behavior of real ants. We deal with the issue of data volume through a clustering heuristic. Dynamic graphs are constructed according to a simulation of colonial odors and pheromone mechanisms. We used numerical databases extracted from the Machine Learning Repository. The experimental results show the effectiveness of the suggested algorithm.
Keywords :
biomimetics; database management systems; graph theory; learning (artificial intelligence); pattern clustering; CL-AntInc; biomimetic method; clustering heuristic; colonial odors; data incremental clustering; data stream; data volume; dynamic graphs; machine learning repository; numerical databases; pheromone mechanisms; real ants behavior; Bismuth; Data models; Databases; Niobium; Vectors; Data analysis; collective intelligence; incremental clustering;
Conference_Titel :
Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on
Conference_Location :
Porto
Print_ISBN :
978-1-4799-5936-5
DOI :
10.1109/NaBIC.2014.6921889
