MicroCluster: efficient deterministic biclustering of microarray data

MicroCluster can mine different types of arbitrarily positioned and overlapping clusters of genetic data to find interesting patterns. Our approach has four key features. First, we mine only the maximal biclusters satisfying certain homogeneity criteria. Second, the clusters can be arbitrarily posit...

Full description

Saved in:
Bibliographic Details
Published in:IEEE intelligent systems 2005-11, Vol.20 (6), p.40-49
Main Authors: Zhao, L., Zaki, M.J.
Format: Article
Language:English
Subjects:
Algorithms
bicluster
Bioinformatics
Circuit noise
Cluster analysis
clustering
Clustering algorithms
Clustering methods
Data mining
Gene expression
Genetics
Heuristic algorithms
microarrays
Testing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:MicroCluster can mine different types of arbitrarily positioned and overlapping clusters of genetic data to find interesting patterns. Our approach has four key features. First, we mine only the maximal biclusters satisfying certain homogeneity criteria. Second, the clusters can be arbitrarily positioned anywhere in the input data matrix, and they can have arbitrary overlapping regions. Third, MicroCluster uses a flexible definition of a cluster that lets it mine several types of biclusters (which previously were studied independently). Finally, MicroCluster can delete or merge biclusters that have large overlaps. So, it can tolerate some noise in the data set and let users focus on the most important clusters. We've developed a set of metrics to evaluate the clustering quality and have tested MicroCluster's effectiveness on several synthetic and real data sets.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2005.112