Fuzzy ants as a clustering concept

Author

Kanade, Parag M. ; Hall, Lawrence O.

Author_Institution

Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA

fYear

2003

fDate

24-26 July 2003

Firstpage

227

Lastpage

232

Abstract

We present a swarm intelligence approach to data clustering. Data is clustered without initial knowledge of the number of clusters. Ant based clustering is used to initially create raw clusters and then these clusters are refined using the Fuzzy C Means algorithm. Initially the ants move the individual objects to form heaps. The centroids of these heaps are taken as the initial cluster centers and the Fuzzy C Means algorithm is used to refine these clusters. In the second stage the objects obtained from the Fuzzy C Means algorithm are hardened according to the maximum membership criteria to form new heaps. These new heaps are then sometimes moved and merged by the ants. The final clusters formed are refined by using the Fuzzy C Means algorithm. Results from three small data sets show that the partitions produced are competitive with those obtained from FCM.

Keywords

data mining; fuzzy set theory; optimisation; pattern clustering; FCM; ant based clustering; clustering concept; data clustering; data sets; fuzzy C means algorithm; fuzzy ants; maximum membership criteria; raw clusters; swarm intelligence; Cadaver; Clustering algorithms; Computer science; Data engineering; Feedback; Fuzzy logic; Iterative algorithms; Particle swarm optimization; Partitioning algorithms; Sorting;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 2003. NAFIPS 2003. 22nd International Conference of the North American

Print_ISBN

0-7803-7918-7

Type

conf

DOI

10.1109/NAFIPS.2003.1226787

Filename

1226787