Penalty term based suitable fuzzy intuitionistic possibilistic clustering: analyzing high dimensional gene expression ca
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Penalty term based suitable fuzzy intuitionistic possibilistic clustering: analyzing high dimensional gene expression cancer database S. R. Kannan1 • Esha Kashyap1 • Mark Last2 • Tzung-Pei Hong3
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The aim of this paper is to identify the co-expressed potential genes that may serve for the development of the portions of normal or tumor. This paper differentiates the co-expressed genes into normal samples and tumor samples from gene expression dataset GSE25066. Since the dataset has vague boundaries and having common characteristics between the clusters, identifying the subgroups contain similar gene expression is really a tricky task one. Therefore, this paper introduces an effective fuzzy iterative clustering algorithm by incorporating kernel function, possibilistic c-means, fuzzy memberships, neighborhood information, median of neighboring objects and penalty term. The performances of the proposed clustering techniques have been shown through the succession experimental works on GSE25066. The effects of clustering results have been proved through comparing the resulted classes with ground truth. Keywords Fuzzy clustering Big data Neighboring objects Cancer database Penalty term
1 Introduction The high numbers of breast cancer among women have increased drastically in the last decade, and it is one of the main leading causes of death among the women. Presently 2,088,849 cancer cases were an estimated around the world, therefore the breast cancer is considered as top cancer of a woman in all the
Communicated by Kannan. & S. R. Kannan [email protected] Mark Last [email protected] http://www.bgu.ac.il/*mlast/ Tzung-Pei Hong [email protected]; http://www.tphong.nuk.edu.tw 1
Department of Mathematics, Pondicherry University, Puducherry, India
2
Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
3
Department of Computer Science and Information Engineering, National University of Kaohsiung, No. 700, Kaohsiung University Road, Nan-Tzu District, Kaohsiung 811, Taiwan
countries. The high numbers of breast cancer among women have increased drastically in the last decade, and it is one of the main leading causes of death among the women (Lestari and Rustam 2017). Therefore, early diagnosis of breast cancer is essential to prevent and cure the diseases, but due to its late detection, the mortality rate is increasing extremely. The research literatures show that the survival rate suffering from breast cancer is significantly enhanced due to early analysis of the disease (Katherine 2001; Patel and Sinha 2010; Sheshadri and Kandaswamy 2006). But the early diagnosis of breast cancer is difficult task that attracts study for researchers over past few years (Agrawal et al. 2019; Chaurasia et al. 2018; Kothari et al. 2018). The above gives attention with researchers to have important applications in cancer research (Haoz
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