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Kernel-Based Fuzzy Intuitionistic Possibilistic Clustering: Analyzing High-Dimensional Gene Expression Cancer Database

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Data-Enabled Discovery and Applications

Abstract

Identifying cohesion of genes for subtypes of diseases in a high-dimensional gene expression database is a highly challenging problem, since the subtypes are based on slight intensity differences between gene expressions. The existed clustering methods are biased with training dataset in identifying the subtypes, and the methods have received irrelevant subtypes. Therefore, this paper introduces unsupervised way of fuzzy clustering to identify the subtypes of genes in a breast cancer database. Here, we have used the dataset which contains 12,634 genes and 288 for finding three available subclasses. In order to cluster the similar intensity genes in the breast cancer dataset, this paper incorporates possibilistic approach, intuitionistic fuzzy sets, and kernel functions with proposed fuzzy clustering techniques. The experimental part of this paper shows that the proposed clustering method how notably identifies the similar gene patterns for common subtypes of breast cancer.

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Funding

This work was financially supported by DST India and MOST Israel.

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Authors and Affiliations

  1. Department of Mathematics, Pondicherry University, Puducherry, India

    Kannan S R & Esha Kashyap

  2. Head of Ben-Gurion University Data Science Research Center, Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Mark Last

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  1. Kannan S R
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Correspondence to Kannan S R.

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R, K.S., Kashyap, E. & Last, M. Kernel-Based Fuzzy Intuitionistic Possibilistic Clustering: Analyzing High-Dimensional Gene Expression Cancer Database. Data-Enabled Discov. Appl. 4, 4 (2020). https://doi.org/10.1007/s41688-020-00039-x

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