Large-scale parallel data clustering

Author

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

Author_Institution

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

Volume

Issue

fYear

1998

fDate

8/1/1998 12:00:00 AM

Firstpage

871

Lastpage

876

Abstract

Algorithmic enhancements are described that enable large computational reduction in mean square-error data clustering. These improvements are incorporated into a parallel data-clustering tool, P-CLUSTER, designed to execute on a network of workstations. Experiments involving the unsupervised segmentation of standard texture images were performed. For some data sets, a 96 percent reduction in computation was achieved

Keywords

image recognition; parallel algorithms; P-CLUSTER; large-scale parallel data clustering; mean square-error data clustering; parallel data-clustering tool; standard texture images; unsupervised segmentation; workstation network; Clustering algorithms; Clustering methods; Data mining; Image processing; Image segmentation; Iterative algorithms; Large-scale systems; Mean square error methods; Sun; Workstations;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.709614

Filename

709614

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1420085