Clustering and analyzing microarray data of lymphoma using singular value decomposition (SVD) and hybrid clustering

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

DNA microarray technology is used to analyze thousands of gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Clustering algorithm has been used as an alternative approach to identify structures from gene expression data. In this paper, we introduce a transform technique based on Singular Value Decomposition (SVD) to identify normalized matrix of gene expression data followed by Partitioning Around Medoids (PAM) to cluster and then displaying the best cluster according to Davis Bouldin Index (DBI) based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard dataset demonstrated the effectiveness of the algorithm in gene expression data.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
Country/TerritoryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • AHC
  • DBI
  • SVD
  • cluster
  • microarray

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