Title :
Parallel clustering on a commodity supercomputer
Author :
Patanè, Giuseppe ; Russo, Marco
Author_Institution :
Fac. of Eng., Catania Univ., Italy
Abstract :
k-means based clustering algorithms have interesting performances in several application fields. The computational complexity of these techniques depends on the size of the data set and the codebook. The larger the data set and the codebook, the greater the computing time to reach the convergence. This paper illustrates the behaviour of two clustering algorithms we have realized and parallelized on a commodity supercomputer
Keywords :
computational complexity; convergence; parallel algorithms; pattern classification; vector quantisation; codebook; commodity supercomputer; computational complexity; convergence; generalised Lloyd algorithm; parallel clustering; unsupervised learning; vector quantisation; Clustering algorithms; Computer science; Convergence; Pattern recognition; Physics; Prototypes; Supercomputers; Unsupervised learning; Vector quantization; Video compression;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861374
