A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets.

ABSTRACT:

Motivation

As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical. There is a requirement to develop algorithms which are fast and can cluster extremely large datasets without affecting the cluster quality. Clustering is an unsupervised exploratory technique applied to microarray data to find similar data structures or expression patterns. Because of the high input/output costs involved and large distance matrices calculated, most of the algomerative clustering algorithms fail on large datasets (30,000 + genes/200 + arrays). In this article, we propose a new two-stage algorithm which partitions the high-dimensional space associated with microarray data using hyperplanes. Th

SUBMITTER: Sharma A 

PROVIDER: S-EPMC2672630 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature