Clustering is a common technique for statistical data analysis. The clustering method based on intuitionistic fuzzy set has attracted more and more scholar’s attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In researchers have used various distance measure like Hamming distance, Euclidean distance etc., to solve the clustering problems. In this paper, we proposed a novel LINEX for intuitionistic fuzzy c means clustering based on minimal spanning tree using Fiedler’s approach initialization method. Our main motives of using the LINEX methods consist inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the integration of datasets, enhancing robustness of the original clustering algorithms to noise and outliers, and still retaining computational simplicity. The proposed Fiedler’s approach LINEX IFCM, which requires the determination of the eigenvector belonging to the second Eigen value of the Laplacian matrix. Finally, evaluation is illustrated by the intuitionistic fuzzy C-means clustering method and the method is compared with the fuzzy C-means clustering method as well.
| Published in |
Applied and Computational Mathematics (Volume 12, Issue 4)
This article belongs to the Special Issue Multiagent Systems with Emerging and Future Applications |
| DOI | 10.11648/j.acm.20231204.11 |
| Page(s) | 82-91 |
| 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 |
Intuitionistic Fuzzy C-means, Fiedler Value, Eigenvalue, Eigenvector, Minimal Spanning Tree, LINEX Function
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APA Style
M. Nithya, K. Bhuvaneswari, S. Senthil. (2023). A Fiedler’s Approach to LINEX Intuitionistic Fuzzy C-means Clustering Induced Spectral Initialization for Data Analysis. Applied and Computational Mathematics, 12(4), 82-91. https://doi.org/10.11648/j.acm.20231204.11
ACS Style
M. Nithya; K. Bhuvaneswari; S. Senthil. A Fiedler’s Approach to LINEX Intuitionistic Fuzzy C-means Clustering Induced Spectral Initialization for Data Analysis. Appl. Comput. Math. 2023, 12(4), 82-91. doi: 10.11648/j.acm.20231204.11
AMA Style
M. Nithya, K. Bhuvaneswari, S. Senthil. A Fiedler’s Approach to LINEX Intuitionistic Fuzzy C-means Clustering Induced Spectral Initialization for Data Analysis. Appl Comput Math. 2023;12(4):82-91. doi: 10.11648/j.acm.20231204.11
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Download@article{10.11648/j.acm.20231204.11,
author = {M. Nithya and K. Bhuvaneswari and S. Senthil},
title = {A Fiedler’s Approach to LINEX Intuitionistic Fuzzy C-means Clustering Induced Spectral Initialization for Data Analysis},
journal = {Applied and Computational Mathematics},
volume = {12},
number = {4},
pages = {82-91},
doi = {10.11648/j.acm.20231204.11},
url = {https://doi.org/10.11648/j.acm.20231204.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20231204.11},
abstract = {Clustering is a common technique for statistical data analysis. The clustering method based on intuitionistic fuzzy set has attracted more and more scholar’s attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In researchers have used various distance measure like Hamming distance, Euclidean distance etc., to solve the clustering problems. In this paper, we proposed a novel LINEX for intuitionistic fuzzy c means clustering based on minimal spanning tree using Fiedler’s approach initialization method. Our main motives of using the LINEX methods consist inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the integration of datasets, enhancing robustness of the original clustering algorithms to noise and outliers, and still retaining computational simplicity. The proposed Fiedler’s approach LINEX IFCM, which requires the determination of the eigenvector belonging to the second Eigen value of the Laplacian matrix. Finally, evaluation is illustrated by the intuitionistic fuzzy C-means clustering method and the method is compared with the fuzzy C-means clustering method as well.},
year = {2023}
}
TY - JOUR T1 - A Fiedler’s Approach to LINEX Intuitionistic Fuzzy C-means Clustering Induced Spectral Initialization for Data Analysis AU - M. Nithya AU - K. Bhuvaneswari AU - S. Senthil Y1 - 2023/07/21 PY - 2023 N1 - https://doi.org/10.11648/j.acm.20231204.11 DO - 10.11648/j.acm.20231204.11 T2 - Applied and Computational Mathematics JF - Applied and Computational Mathematics JO - Applied and Computational Mathematics SP - 82 EP - 91 PB - Science Publishing Group SN - 2328-5613 UR - https://doi.org/10.11648/j.acm.20231204.11 AB - Clustering is a common technique for statistical data analysis. The clustering method based on intuitionistic fuzzy set has attracted more and more scholar’s attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In researchers have used various distance measure like Hamming distance, Euclidean distance etc., to solve the clustering problems. In this paper, we proposed a novel LINEX for intuitionistic fuzzy c means clustering based on minimal spanning tree using Fiedler’s approach initialization method. Our main motives of using the LINEX methods consist inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the integration of datasets, enhancing robustness of the original clustering algorithms to noise and outliers, and still retaining computational simplicity. The proposed Fiedler’s approach LINEX IFCM, which requires the determination of the eigenvector belonging to the second Eigen value of the Laplacian matrix. Finally, evaluation is illustrated by the intuitionistic fuzzy C-means clustering method and the method is compared with the fuzzy C-means clustering method as well. VL - 12 IS - 4 ER -
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