A modified hyper plane clustering algorithm allows for efficient and accurate
clustering of extremely large datasets.
Authors Sharma A, Podolsky R, Zhao J, McIndoe RA.
Submitted By Richard McIndoe on 3/6/2009
Status Published
Journal Bioinformatics (Oxford, England)
Year 2009
Date Published 5/1/2009
Volume : Pages Not Specified : Not Specified
PubMed Reference 19261720
Abstract Motivation: As the number of publically available microarray experiments
increases, the ability to analyze extremely large data sets 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 I/O costs involved and large distance matrices calculated, most of
the algomerative clustering algorithms fail on large datasets (30,000+
genes/200+ arrays). In this paper we propose a new two-stage algorithm which
partitions the high dimensional space associated with microarray data using
hyper planes. The first stage is based on the BIRCH (Balanced Iterative Reducing
and Clustering using Hierarchies) algorithm with the second stage being a
conventional k-Means clustering technique. This algorithm has been implemented
in a software tool (HPCluster) designed to cluster gene expression data. We
compared the clustering results using the two stage hyper plane algorithm with
the conventional k-Means algorithm from other available programs. Because the
first stage traverses the data in a single scan, the performance and speed
increases substantially. The data reduction accomplished in the first stage of
the algorithm reduces the memory requirements allowing us to cluster 44,460
genes without failure and significantly decreases the time to complete when
compared to popular k-Means programs. The software was written in C# (.NET 1.1).
Availability: The program is freely available and can be downloaded from

Investigators with authorship
Richard McIndoeAugusta University