International Journal of Intelligent Information Systems

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Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms

Received: 10 October 2014    Accepted: 14 October 2014    Published: 06 November 2014
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Abstract

In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in “Big Data” where computation time is equally or more important than accuracy. However, there is a trade-off between computation time (speed) and accuracy among clustering algorithms. Specifically, general type-2 fuzzy c-means (GT2 FCM) is considered to be a highly accurate clustering approach, but it is computationally intensive. To improve its computation time we propose a hybrid clustering algorithm called KFGT2FCM that combines GT2 FCM with two fast algorithms k-means and Fuzzy C-means algorithm for input data preprocessing of classification algorithms. The proposed algorithm shows improved computation time when compared with GT2 FCM on five benchmarks from university of California Irvine (UCI) library.

DOI 10.11648/j.ijiis.s.2014030601.27
Published in International Journal of Intelligent Information Systems (Volume 3, Issue 6-1, December 2014)

This article belongs to the Special Issue Research and Practices in Information Systems and Technologies in Developing Countries

Page(s) 91-97
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Classification, Input Data Preprocessing, Clustering, General Type-2 Fuzzy Logic, Fuzzy C-Means (FCM), K-Means

References
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Author Information
  • Department of Computer Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran

  • Department of Electrical Engineering, University of Neyshabur, Neyshabur, Iran

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  • APA Style

    Vahid Nouri, Mohammad Reza Akbarzadeh, Tootoonchi, Alireza Rowhanimanesh. (2014). Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms. International Journal of Intelligent Information Systems, 3(6-1), 91-97. https://doi.org/10.11648/j.ijiis.s.2014030601.27

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    ACS Style

    Vahid Nouri; Mohammad Reza Akbarzadeh; Tootoonchi; Alireza Rowhanimanesh. Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms. Int. J. Intell. Inf. Syst. 2014, 3(6-1), 91-97. doi: 10.11648/j.ijiis.s.2014030601.27

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    AMA Style

    Vahid Nouri, Mohammad Reza Akbarzadeh, Tootoonchi, Alireza Rowhanimanesh. Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms. Int J Intell Inf Syst. 2014;3(6-1):91-97. doi: 10.11648/j.ijiis.s.2014030601.27

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  • @article{10.11648/j.ijiis.s.2014030601.27,
      author = {Vahid Nouri and Mohammad Reza Akbarzadeh and Tootoonchi and Alireza Rowhanimanesh},
      title = {Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms},
      journal = {International Journal of Intelligent Information Systems},
      volume = {3},
      number = {6-1},
      pages = {91-97},
      doi = {10.11648/j.ijiis.s.2014030601.27},
      url = {https://doi.org/10.11648/j.ijiis.s.2014030601.27},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijiis.s.2014030601.27},
      abstract = {In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in “Big Data” where computation time is equally or more important than accuracy. However, there is a trade-off between computation time (speed) and accuracy among clustering algorithms. Specifically, general type-2 fuzzy c-means (GT2 FCM) is considered to be a highly accurate clustering approach, but it is computationally intensive. To improve its computation time we propose a hybrid clustering algorithm called KFGT2FCM that combines GT2 FCM with two fast algorithms k-means and Fuzzy C-means algorithm for input data preprocessing of classification algorithms. The proposed algorithm shows improved computation time when compared with GT2 FCM on five benchmarks from university of California Irvine (UCI) library.},
     year = {2014}
    }
    

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    AU  - Vahid Nouri
    AU  - Mohammad Reza Akbarzadeh
    AU  - Tootoonchi
    AU  - Alireza Rowhanimanesh
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    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
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    AB  - In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in “Big Data” where computation time is equally or more important than accuracy. However, there is a trade-off between computation time (speed) and accuracy among clustering algorithms. Specifically, general type-2 fuzzy c-means (GT2 FCM) is considered to be a highly accurate clustering approach, but it is computationally intensive. To improve its computation time we propose a hybrid clustering algorithm called KFGT2FCM that combines GT2 FCM with two fast algorithms k-means and Fuzzy C-means algorithm for input data preprocessing of classification algorithms. The proposed algorithm shows improved computation time when compared with GT2 FCM on five benchmarks from university of California Irvine (UCI) library.
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