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Extension of Fuzzy Principal Component Analysis to Type-2 Fuzzy Principal Component Analysis

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Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 425))

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Abstract

This chapter compares type 1 and type 2 fuzzy principal component analysis which are based on type 1 and type 2 fuzzy C-means algorithms, respectively. The two clustering methods are the combination of k-means clustering algorithm and type 1 and type 2 fuzzy logic, respectively.

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Authors and Affiliations

  1. LPAIS, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Daoudi Bouchra, Hamzaoui Hassania & Gouiouez Mounir

Authors
  1. Daoudi Bouchra
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  2. Hamzaoui Hassania
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  3. Gouiouez Mounir
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Corresponding author

Correspondence to Daoudi Bouchra .

Editor information

Editors and Affiliations

  1. Research Chair of Graduate Studies, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Patna, India

    Anupam Kumar

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Bouchra, D., Hassania, H., Mounir, G. (2023). Extension of Fuzzy Principal Component Analysis to Type-2 Fuzzy Principal Component Analysis. In: Castillo, O., Kumar, A. (eds) Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications. Studies in Fuzziness and Soft Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-031-26332-3_16

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