Cancer tissue sample classification using point symmetry-based clustering algorithm

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.

A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark

“…It operates over datasets with no prior information about labels of data objects (Berry & Browne, 2006). To improve the quality of clustering solutions, clustering methods developed different objective functions to improve the assessment of the homogeneity of data objects in each cluster and the separation among clusters (Maulik, Mukhopadhyay & Bandyopadhyay, 2009;Bandyopadhyay, Mukhopadhyay & Maulik, 2007;Maulik, Bandyopadhyay & Mukhopadhyay, 2011;Acharya & Saha, 2018; Parraga-Alava, Dorn & Inostroza-Ponta, Table 1 An example of the first set of rows of the human fibroblasts serum microarray dataset (Maulik, Mukhopadhyay & Bandyopadhyay). 2018).…”

A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization

“…MOPs can be found in multiple fields, such as engineering [27] and bioinformatics [3]. They have multiple objectives to be optimized.…”

“…Currently, real-life problems share a common aspect, which have multiple objectives to be optimized. Problems, such as recommender systems [1], industry [2] and bioinformatics [3] have many objectives that need to be optimized simultaneously to reach an effective solution. These objectives are often contradictory, so optimizing one objective deteriorates the degree of optimization of other objectives.…”