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A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering

Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on spec...

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Detalles Bibliográficos
Autores principales: Rajadurai, Priscilla, Sankaranarayanan, Swamynathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3511054/
https://www.ncbi.nlm.nih.gov/pubmed/23226009
http://dx.doi.org/10.4137/BBI.S10383
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author Rajadurai, Priscilla
Sankaranarayanan, Swamynathan
author_facet Rajadurai, Priscilla
Sankaranarayanan, Swamynathan
author_sort Rajadurai, Priscilla
collection PubMed
description Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on specified features. Euclidean distance is a widely used similarity measure for gene expression data that considers the amount of changes in gene expression. However, the huge number of genes and the intricacy of biological networks have highly increased the challenges of comprehending and interpreting the resulting group of data, increasing processing time. The proposed technique focuses on a QT based fast 2-dimensional hierarchical clustering algorithm to perform clustering. The construction of the closest pair data structure is an each level is an important time factor, which determines the processing time of clustering. The proposed model reduces the processing time and improves analysis of gene expression data.
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spelling pubmed-35110542012-12-05 A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering Rajadurai, Priscilla Sankaranarayanan, Swamynathan Bioinform Biol Insights Original Research Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on specified features. Euclidean distance is a widely used similarity measure for gene expression data that considers the amount of changes in gene expression. However, the huge number of genes and the intricacy of biological networks have highly increased the challenges of comprehending and interpreting the resulting group of data, increasing processing time. The proposed technique focuses on a QT based fast 2-dimensional hierarchical clustering algorithm to perform clustering. The construction of the closest pair data structure is an each level is an important time factor, which determines the processing time of clustering. The proposed model reduces the processing time and improves analysis of gene expression data. Libertas Academica 2012-11-19 /pmc/articles/PMC3511054/ /pubmed/23226009 http://dx.doi.org/10.4137/BBI.S10383 Text en © 2012 the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.
spellingShingle Original Research
Rajadurai, Priscilla
Sankaranarayanan, Swamynathan
A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering
title A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering
title_full A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering
title_fullStr A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering
title_full_unstemmed A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering
title_short A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering
title_sort fast quad-tree based two dimensional hierarchical clustering
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3511054/
https://www.ncbi.nlm.nih.gov/pubmed/23226009
http://dx.doi.org/10.4137/BBI.S10383
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