ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use.

ABSTRACT:

Background

During the last decade, the use of microarrays to assess the transcriptome of many biological systems has generated an enormous amount of data. A common technique used to organize and analyze microarray data is to perform cluster analysis. While many clustering algorithms have been developed, they all suffer a significant decrease in computational performance as the size of the dataset being analyzed becomes very large. For example, clustering 10000 genes from an experiment containing 200 microarrays can be quite time consuming and challenging on a desktop PC. One solution to the scalability problem of clustering algorithms is to distribute or parallelize the algorithm across multiple computers.

Results

The software described in this paper is a high performance mu

SUBMITTER: Kraj P 

PROVIDER: S-EPMC2375128 | biostudies-literature | 2008 Apr

REPOSITORIES: biostudies-literature