A vector space model for automatic indexing
Communications of the ACM
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
THEA: ontology-driven analysis of microarray data
Bioinformatics
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Bioinformatics is the science of managing, mining and interpreting information from biological sequences and structures. DNA Microarrays, also known as gene chips, provide an effective tool for monitoring and profiling gene expression patterns by measuring the expression levels of thousands of genes simultaneously. Clustering is a popular technique for microarray data to finding groups of genes with similar functionalities based on GO Ontology. In this paper, data mining technique, clustering is used on microarray data to group genes with similar functionalities based on Go ontology. Gene Ontology is used to provide external validation for the clusters to determine if the genes in a cluster belong to a specific Biological Process, Cellular Component and Molecular Function. A functionally meaningful cluster contains many genes that are annotated to a specific GO terms. To prove that each of these new cluster sets reveal biological associations that were not apparent from clustering the original gene expression data.