Title :
The K-means clustering algorithm based on density and ant colony
Author :
Yuqing, Peng ; Xiangdan, Hou ; Shang, Liu
Author_Institution :
Sch. of Comput. Sci. & Eng., Hebei Univ. of Technol., Tianjin, China
Abstract :
The ant algorithm is a new evolutional method, k-means and the density-cluster are familiar cluster analysis. In this paper, we proposed a new K-means algorithm based on density and ant theory, which resolved the problem of local minimal by the random of ants and handled the initial parameter sensitivity of k-means. In addition it combined idea of density and made the ants searching selectable. With the experiments it was proved that the algorithm we proposed improved the efficiency and precision of cluster.
Keywords :
data mining; pattern clustering; sensitivity; K-means clustering algorithm; ant colony; cluster analysis; density; density-cluster; parameter sensitivity; Algorithm design and analysis; Clustering algorithms; Computer science; Data mining; Geography; Home appliances; Neural networks; Partitioning algorithms; Statistical analysis; Wavelet analysis;
Conference_Titel :
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
0-7803-7702-8
DOI :
10.1109/ICNNSP.2003.1279307
