DocumentCode :
2336896
Title :
A new ant colony clustering algorithm based on DBSCAN
Author :
Liu, Shang ; Dou, Zhi-Tong ; Li, Fei ; Huang, Ya-lou
Author_Institution :
Coll. of Inf. Sci. & Technol., Nankai Univ., Tianjin, China
Volume :
3
fYear :
2004
fDate :
26-29 Aug. 2004
Firstpage :
1491
Abstract :
The AntClass algorithm is a new algorithm applying ant colony clustering algorithm to cluster analysis, and the result is satisfying. To attack the slow speed of the AntClass algorithm, a new algorithm named DBAntCluster is proposed. Firstly, the high density clusters are got in the dataset by using DBSCAN algorithm, and then these high density clusters are scattered in the grid board as a special kind of data object with other single data objects in the dataset. In DBAntCluster algorithm, the ants can avoid many unnecessary movements by using the data attribute of density and distribution well, and the speed is greatly accelerated. This improvement is validated in our experiments.
Keywords :
pattern clustering; statistical analysis; DBAntCluster algorithm; DBSCAN algorithm; ant colony clustering algorithm; high density cluster analysis; Acceleration; Algorithm design and analysis; Ant colony optimization; Clustering algorithms; Educational institutions; Information processing; Information science; Laboratories; Motion analysis; Scattering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
Type :
conf
DOI :
10.1109/ICMLC.2004.1382009
Filename :
1382009
Link To Document :
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