Large-scale parallel data clustering

Author

Judd, Dan ; McKinley, Philip K. ; Jain, Anil K.

Author_Institution

Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI, USA

Volume

4

fYear

1996

fDate

25-29 Aug 1996

Firstpage

488

Abstract

Algorithmic enhancements are described that allow large reduction (for some data sets, over 95 percent) in the number of floating point operations in mean square error data clustering. These improvements are incorporated into a parallel data clustering tool, P-CLUSTER, developed in an earlier study. Experiments on segmenting standard texture images show that the proposed enhancements enable clustering of an entire 512×512 image at approximately the same computational cost as that of previous methods applied to only 5 percent of the image pixels

Keywords

computational complexity; image recognition; image segmentation; image texture; parallel processing; 262144 pixel; 512 pixel; P-CLUSTER; floating point operations; large-scale parallel data clustering; mean square error data clustering; standard texture image segmentation; Clustering algorithms; Clustering methods; Computational efficiency; Computer errors; Computer science; Image segmentation; Large-scale systems; Partitioning algorithms; Pixel; Workstations;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1996., Proceedings of the 13th International Conference on

Conference_Location

Vienna

ISSN

1051-4651

Print_ISBN

0-8186-7282-X

Type

conf

DOI

10.1109/ICPR.1996.547613

Filename

547613