Prospects and Possibilities for Future Research of Fuzzy C-Means (FCM)

Authors

  • Samsul Arifin Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Yoga Virya Arya Anandha Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Edrick Setiawan Setiawan Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Terrance Dave Phoebus Mathematics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Stanley Jonathan Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Melody Effendi Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Michael Evan Setyawan Primary Teacher Education Department, Faculty of Humanities, Bina Nusantara University. Jakarta. Indonesia.
  • Kevin Laurent Oktavian Putra Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia

Keywords:

Bibliometric analysis, Fuzzy C-Means (FCM), Vosviewer, Scopus Web

Abstract

Bibliometric research has an important role in identifying trends, topics, and influences of a field of study through quantitative analysis of scientific publications. One of the popular data grouping algorithms in bibliometric analysis is Fuzzy C-Means (FCM). However, most bibliometric studies using FCM rely only on grouping the data without conducting further analysis. Therefore, this study aims to apply bibliometric analysis to FCM using VOSviewer. The data for this study were taken from the Scopus database and selected using predetermined selection criteria. A total of 103 documents were selected for analysis using FCM and VOSviewer. The results of the analysis show that FCM can group scientific publications related to Fuzzy C-Means into four groups. These groups were further analyzed using VOSviewer to identify the main topics and relationships between topics. Bibliometric analysis shows that the most dominant topic in FCM research is the application in image processing, with sub-topics such as pixel grouping and image segmentation. In addition, the results of the analysis also show that there is a close relationship between the topic of FCM and the topic of natural language processing and fuzzy logic. This study shows that FCM has great potential in bibliometric analysis, especially in classifying and identifying the main topics of scientific publications. The use of VOSviewer in the bibliometric analysis also helps in describing and visualizing the analysis results more clearly and easily understood. This research can pave the way for further research on the application of FCM in other fields of study as well as the development of more sophisticated and effective methods of bibliometric analysis.

Downloads

Download data is not yet available.

References

J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy c-means clustering algorithm,” Comput. Geosci., vol. 10, no. 2–3, pp. 191–203, 1984.

J. Zhu, X. Ma, L. Martínez, and J. Zhan, “A Probabilistic Linguistic Three-Way Decision Method With Regret Theory via Fuzzy C-Means Clustering Algorithm,” IEEE Trans. Fuzzy Syst., 2023.

N. Van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010.

S. Arifin and I. B. Muktyas, “Generate a system of linear equation through unimodular matrix using Python and Latex,” in AIP Conference Proceedings, Apr. 2021, vol. 2331. doi: 10.1063/5.0041651.

Q. He and R. Pan, “Automatic segmentation algorithm for magnetic resonance imaging in prediction of breast tumour histological grading,” Expert Syst., 2021, doi: 10.1111/exsy.12846.

J. A. M. Muñoz, E. H. Viedma, A. L. S. Espejo, and M. J. Cobo, “Software tools for conducting bibliometric analysis in science: An up-to-date review,” El Prof. la Inf., vol. 29, no. 1, p. 4, 2020.

E. Orduña-Malea and R. Costas, “Link-based approach to study scientific software usage: The case of VOSviewer,” Scientometrics, vol. 126, no. 9, pp. 8153–8186, 2021.

R. Suganya and R. Shanthi, “Fuzzy c-means algorithm-a review,” Int. J. Sci. Res. Publ., vol. 2, no. 11, p. 1, 2012.

S. Yin and H. Li, “Hot region selection based on selective search and modified fuzzy C-means in remote sensing images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 5862–5871, 2020.

S. Arifin and I. B. Muktyas, “Membangkitkan Suatu Matriks Unimodular Dengan Python,” J. Deriv. J. Mat. dan Pendidik. Mat., vol. 5, no. 2, pp. 1–10, 2018.

R. S. Bankar and S. R. Lihitkar, “Science mapping and visualization tools used for bibliometric and scientometric studies: A comparative study,” J. Adv. Libr. Sci., vol. 6, no. 1, pp. 382–394, 2019.

S. Hwang, J. Park, J. Won, Y. Kwon, and Y. Kim, “Object detection for cargo unloading system based on fuzzy C means,” C. Mater. Contin, vol. 71, pp. 4167–4181, 2022.

T. D. Khang, N. D. Vuong, M.-K. Tran, and M. Fowler, “Fuzzy C-means clustering algorithm with multiple fuzzification coefficients,” Algorithms, vol. 13, no. 7, p. 158, 2020.

R. Saxena and A. Husain, “Performance Enhancement in WSN Through Fuzzy C-Means Based Hybrid Clustering (FCMHC),” in Advancements in Smart Computing and Information Security: First International Conference, ASCIS 2022, Rajkot, India, November 24–26, 2022, Revised Selected Papers, Part I, 2023, pp. 62–76.

Elsevier, “Scopus.” https://www.scopus.com/

T. Handayani, S. Arifin, and A. Surgandini, “Penerapan Model Pembelajaran Penemuan Terbimbing Untuk Meningkatkan Kemampuan Pemahaman Konsep Matematis Siswa SMA,” Wacana Akad., vol. 3, no. 2, pp. 151–164, 2019.

J. Nayak, B. Naik, and Hs. Behera, “Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014,” in Computational Intelligence in Data Mining-Volume 2: Proceedings of the International Conference on CIDM, 20-21 December 2014, 2015, pp. 133–149.

A. E. Ezugwu, A. K. Shukla, M. B. Agbaje, O. N. Oyelade, A. José-García, and J. O. Agushaka, “Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature,” Neural Comput. Appl., vol. 33, pp. 6247–6306, 2021.

B. Rahman, S. Arifin, and I. Muktyas, “All cyclic subgroups in group (Zm × zn, +) using python,” Int. J. Sci. Technol. Res., vol. 8, no. 9, pp. 2282–2285, 2019.

S. Chakim, “Bibliometric Analysis: Symbolic Power Publication Trens in Scopus. com,” 2022.

S. Halder, S. Bhattacharya, M. B. Roy, and P. K. Roy, “Application of fuzzy C-means clustering and fuzzy EDAS to assess groundwater irrigation suitability and prioritization for agricultural development in a complex hydrogeological basin,” Environ. Sci. Pollut. Res., pp. 1–29, 2023.

E. Herrera-Viedma, M. A. Martinez, and M. Herrera, “Bibliometric tools for discovering information in database,” in Trends in Applied Knowledge-Based Systems and Data Science: 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings 29, 2016, pp. 193–203.

S. Arifin, I. Bayu Muktyas, and K. Iswara Sukmawati, “Product of two groups integers modulo m,n and their factor groups using python,” in Journal of Physics: Conference Series, Mar. 2021, vol. 1778, no. 1. doi: 10.1088/1742-6596/1778/1/012026.

F. Valdez, O. Castillo, and P. Melin, “Bio-inspired algorithms and its applications for optimization in fuzzy clustering,” Algorithms, vol. 14, no. 4, p. 122, 2021.

I. B. Muktyas, Sulistiawati, and S. Arifin, “Digital image encryption algorithm through unimodular matrix and logistic map using Python,” in AIP Conference Proceedings, Apr. 2021, vol. 2331. doi: 10.1063/5.0041653.

L. Sun, L. Wang, W. Ding, Y. Qian, and J. Xu, “Feature selection using fuzzy neighborhood entropy-based uncertainty measures for fuzzy neighborhood multigranulation rough sets,” IEEE Trans. Fuzzy Syst., vol. 29, no. 1, pp. 19–33, 2020.

J. Zhao, G. Wang, J.-S. Pan, T. Fan, and I. Lee, “Density peaks clustering algorithm based on fuzzy and weighted shared neighbor for uneven density datasets,” Pattern Recognit., vol. 139, 2023, doi: 10.1016/j.patcog.2023.109406.

J. Jin, H. Garg, and T. You, “Generalized picture fuzzy distance and similarity measures on the complete lattice and their applications,” Expert Syst. Appl., vol. 220, 2023, doi: 10.1016/j.eswa.2023.119710.

F. Xiao, “A distance measure for intuitionistic fuzzy sets and its application to pattern classification problems,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 51, no. 6, pp. 3980–3992, 2019.

Y. Wang et al., “Random Feature based Collaborative Kernel Fuzzy Clustering for Distributed Peer-to-Peer Networks,” IEEE Trans. Fuzzy Syst., 2022.

Visualization by author

Downloads

Published

17.02.2023

How to Cite

Arifin, S. ., Anandha, Y. V. A. ., Setiawan, E. S., Phoebus, T. D. ., Jonathan, S. ., Effendi, M. ., Setyawan, M. E. ., & Oktavian Putra, K. L. . (2023). Prospects and Possibilities for Future Research of Fuzzy C-Means (FCM). International Journal of Intelligent Systems and Applications in Engineering, 11(2), 741–751. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2848

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.