Improved T-Cluster based scheme for combination gene scale expression data

Clustering is an unsupervised learning technique in that there is no explicit demarcation of data as training and test data. Clustering aims to group related records by measuring similarities among the attribute. Major phase of clustering techniques is similarity measurement and it is based on diffe...

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Bibliographic Details
Main Authors: Vengatesan, K., Selvarajan, S.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Clustering algorithms
Correlation coefficient
EM Algorithms
Euclidean distance
Gene expression
Non Negative Matrix Factorization
Tanimoto Correlation Coefficient
TCLUST
Translation
Vectors
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Summary:Clustering is an unsupervised learning technique in that there is no explicit demarcation of data as training and test data. Clustering aims to group related records by measuring similarities among the attribute. Major phase of clustering techniques is similarity measurement and it is based on different factors and parameters. The improved Nonnegative Matrix Factorization (NMF) based TCLUST (T-Clustering) algorithm is EM principle (Expectation Maximization) based algorithm, intended to search for approximate solutions. The EM algorithm is the efficient method of obtaining a solution to the mixture likelihood problem. Genes with a common function are often hypothesized to have correlated expression levels across different conditions. NMF clustering is introduced to find a small number of Meta genes, each defined as a positive linear combination of the genes in the expression data. The proposed clustering algorithm is applied to a genome scale gene expression dataset to enrichment analysis and to discover highly significant biological clusters.
DOI:10.1109/ICRCC.2012.6450562