@inproceedings{ea90b09302ab413bbc4c5bff6e095fac,
title = "Clustering and analyzing microarray data of lymphoma using singular value decomposition (SVD) and hybrid clustering",
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.",
keywords = "AHC, DBI, SVD, cluster, microarray",
author = "A. Bustamam and S. Formalidin and T. Siswantining",
note = "Funding Information: This research is supported by PITTA UI 2017 research grant from Universitas Indonesia. Publisher Copyright: {\textcopyright} 2018 Author(s).; 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 ; Conference date: 26-07-2017 Through 27-07-2017",
year = "2018",
month = oct,
day = "22",
doi = "10.1063/1.5064217",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Ratna Yuniati and Terry Mart and Anggraningrum, {Ivandini T.} and Djoko Triyono and Sugeng, {Kiki A.}",
booktitle = "Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017",
}