DocumentCode
353291
Title
Parallel clustering on a commodity supercomputer
Author
Patanè, Giuseppe ; Russo, Marco
Author_Institution
Fac. of Eng., Catania Univ., Italy
Volume
3
fYear
2000
fDate
2000
Firstpage
575
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.861374
Filename
861374
Link To Document