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A Noise Resistant Credibilistic Fuzzy Clustering Algorithm on a Unit Hypersphere with Illustrations Using Expression Data

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Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 149))

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Abstract

This article presents a robust noise-resistant fuzzy-based algorithm for cancer class detection. High-throughput microarray technologies facilitate the generation of large-scale expression data; this data captures enough information to build classifiers to understand the molecular basis of a disease. The proposed approach built on the Credibilistic Fuzzy C-Means (CFCM) algorithm partitions data restricted to a p-dimensional unit hypersphere. CFCM was introduced to address the noise sensitiveness of fuzzy-based procedures, but it is unstable and fails to capture local non-linear interactions. The introduced approach addresses these shortcomings. The experimental findings in this article focus on cancer expression datasets. The performance of the proposed approach is assessed with both internal and external measures. The fuzzy-based learning algorithms Fuzzy C-Means (FCM) and Hyperspherical Fuzzy C-Means (HFCM) are used for comparative analysis. The experimental findings indicate that the proposed approach can be used as a plausible tool for clustering cancer expression data.

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Acknowledgment

Oleksii K. Tyshchenko acknowledges the Helmholtz Information and Data Science Academy (HIDA) for providing financial support enabling a short-term research stay at Helmholtz Centre for Infection Research (Braunschweig, Germany) for fuzzy clustering analysis of biological data.

Author information

Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, China

    Zhengbing Hu

  2. Information Systems Engineering and Software Engineering, Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Mark Last

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. National Sun Yat-sen University, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Institute for Research and Applications of Fuzzy Modeling, CE IT4Innovations, University of Ostrava, Ostrava, Czech Republic

    Oleksii K. Tyshchenko

  6. Ramanujan School of Mathematical Sciences, Pondicherry University, Puducherry, India

    Esha Kashyap

Authors
  1. Zhengbing Hu
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  2. Mark Last
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  3. Tzung-Pei Hong
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  4. Oleksii K. Tyshchenko
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  5. Esha Kashyap
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Corresponding author

Correspondence to Oleksii K. Tyshchenko .

Editor information

Editors and Affiliations

  1. Jan Evangelista Purkyně University in Ústi nad Labem, Ústi nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Hu, Z., Last, M., Hong, TP., Tyshchenko, O.K., Kashyap, E. (2023). A Noise Resistant Credibilistic Fuzzy Clustering Algorithm on a Unit Hypersphere with Illustrations Using Expression Data. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_32

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