AntTree: a new model for clustering with artificial ants

Author

Azzag, Hanane ; Monmarche, N. ; Slimane, M. ; Venturini, Gilles

Author_Institution

Lab. d´Informatique, Ecole Polytech. de l´Univ. de Tours, France

Volume

4

fYear

2003

fDate

8-12 Dec. 2003

Firstpage

2642

Abstract

We present a new clustering algorithm for unsupervised learning. It is inspired from the self-assembling behavior observed in real ants where ants progressively become attached to an existing support and then successively to other attached ants. The artificial ants that we have defined similarly builds a tree. Each ant represents one data. The way ants move and build this tree depends on the similarity between the data. We have compared our results to those obtained by the k-means algorithm and by AntClass on numerical databases (either artificial, real, or from the CE.R.I.E.S.). We show that AntTree significantly improves the clustering process.

Keywords

computational complexity; statistical analysis; tree data structures; trees (mathematics); unsupervised learning; AntClass; AntTree; artificial ant; clustering algorithm; k-means algorithm; numerical database; self-assembling behavior; unsupervised learning; Biological system modeling; CADCAM; Clustering algorithms; Computer aided manufacturing; Data mining; Databases; Microorganisms; Sorting; US Department of Transportation; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2003. CEC '03. The 2003 Congress on

Print_ISBN

0-7803-7804-0

Type

conf

DOI

10.1109/CEC.2003.1299421

Filename

1299421