Parallel pruning for k-means clustering on shared memory architectures

Date
2001
Advisor
Instructor
Source Title
Euro-Par 2001 Parallel Processing
Print ISSN
0302-9743
Electronic ISSN
Publisher
Springer Verlag
Volume
2150
Issue
Pages
321 - 325
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

We have developed and evaluated two parallelization schemes for a tree-based k-means clustering method on shared memory machines. One scheme is to partition the pattern space across processors. We have determined that spatial decomposition of patterns outperforms random decomposition even though random decomposition has almost no load imbalance problem. The other scheme is the parallel traverse of the search tree. This approach solves the load imbalance problem and performs slightly better than the spatial decomposition, but the efficiency is reduced due to thread synchronizations. In both cases, parallel treebased k-means clustering is significantly faster than the direct parallel k-means. © Springer-Verlag Berlin Heidelberg 2001.

Course
Other identifiers
Book Title
Keywords
Clustering algorithms, Distributed computer systems, Parallel architectures, K-means clustering, K-means clustering method, Parallelizations, Random decomposition, Shared memory architecture, Shared memory machines, Spatial decompositions, Thread synchronization, Memory architecture
Citation
Published Version (Please cite this version)