Clustering of Mitochondrial D-loop Sequences Using Similarity Matrix, PCA and K-means Algorithm

Can Eyüpoğlu

Abstract

In this study, mitochondrial displacement-loop (D-loop) sequences isolated from different hominid species are clustered using similarity matrix, Principal Component Analysis (PCA) and K-means algorithm. Firstly, the mitochondrial D-loop sequence data are retrieved from the GenBank database and copied into MATLAB. Pairwise distances are computed using p distance and Jukes-Cantor methods. A phylogenetic tree is created and then a similarity matrix is generated according to the pairwise distances. Furthermore, the clustering is performed using only K-means algorithm. After that PCA and K-means are used together in order to cluster mitochondrial D-loop sequences.

Keywords

Clustering;p-distance;PCA;Jukes-Cantor;K-means algorithm;Similarity matrix

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Submitted: 2018-12-21 11:28:41
Published: 2016-12-26 00:00:00
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