Title :
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
fDate :
8/1/1998 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
