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Title: Cancer tissue sample classification using point symmetry-based clustering algorithm

Authors: Sudipta Acharya; Sriparna Saha

Addresses: Department of Computer Science and Engineering, IIT Patna, India ' Department of Computer Science and Engineering, IIT Patna, India

Abstract: Clustering or unsupervised classification techniques can be used to solve different types of classification problems of different domains. Symmetry is an important property for any real life object. Therefore, symmetry-based distance measurements play some important roles in identifying some patterns or clusters of real life datasets. In this paper, inspired by the symmetric property, we have proposed a point symmetry-based clustering algorithm which has been used to identify clusters of tissue samples from some real life cancer datasets. Our proposed algorithm is also multi-objective-optimisation (MOO) based, i.e., optimises more than one objectives simultaneously. We have also shown the superiority of our proposed algorithm with respect to some state-of-the-art clustering algorithms.

Keywords: multi-objective-optimisation; MOO; clustering; AMOSA; gene marker; point symmetry-based distance; ARI index; %CoA index.

DOI: 10.1504/IJHT.2018.090282

International Journal of Humanitarian Technology, 2018 Vol.1 No.1, pp.19 - 39

Received: 27 Jan 2015
Accepted: 13 Sep 2015

Published online: 01 Mar 2018 *

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