A Complete Intuitionistic Fuzzy C-Means Clustering Method

Dung Thi Thu Nguyen

Abstract


Currently, data clustering methods play an important role in the field of data mining. However, the development of new methods is required to increase the efficiency of processing and increase the effectiveness of clustering. Today, classification methods are being improved, and positive results have been obtained when using intuitionistic fuzzy logic. However, this technique is new and requires the development of new methods and algorithms to increase the reliability of the results. In this paper, a method of intuitionistic fuzzy C-means clustering is developed based on a modification of the objective function that takes into account the intuitionistic fuzzy index. The proposed method was tested in solving the problem of image segmentation. Image segmentation results are evaluated and compared with other methods, showing that the proposed method produces better results than existing methods.

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References


T. T. Z. Nguen and L. V. Chernen'kaja, “Sistemnyj analiz v upravlenii razvitiem territorial'nyh kompleksov v'etnama,” in V sbornike: Sistemnyj analiz v proektirovanii i upravlenii. sbornik nauchnyh trudov XXV Mezhdunarodnoj nauchnoj i uchebno-prakticheskoj konferencii, SPB, 2021, pp. 346–352.

T. T. Z. Nguen and L. V. Chernen'kaja, “Model' analiza faktorov na osnove nechetkoj klasterizacii s-srednih,” in Zhurnal Izvestija Tul'skogo gosudarstvennogo universiteta – Tehnicheskie nauki (TulGU, g. Tula), no. 1, Tula: Izdatel'stve TulGu, 2023, pp. 329–336.

T. T. Z. Nguen and L. V. Chernen'kaja, “Jevristicheskaja nechetkaja model' prognozirovanija vysokogo porjadka s hedzh-algebroj,” in Sistemnyj analiz v proektirovanii i upravlenii, 2023.

T. T. Z. Nguen and L. V. Chernen'kaja, “Novaja gaussovskaja sistema nechjotkogo logicheskogo vyvoda tipa Takagi-Sugeno-Kanga s vesom glavnogo komponenta,” International Journal of Open Information Technologies, vol. 12, no. 5, pp. 116–124, 2024.

S. S. Khan and A. Ahmad, “Cluster center initialization algorithm for K-means clustering,” Pattern Recognit Lett, vol. 25, no. 11, pp. 1293–1302, 2004, doi: 10.1016/j.patrec.2004.04.007.

Nguen Thi Thu Zung and L. V. Chernen'kaja, “Diskretizacija v modeljah prognozirovanija nechetkih vremennyh rjadov,” Zhurnal Izvestija Tul'skogo gosudarstvennogo universiteta – Tehnicheskie nauki (TulGU, g. Tula), vol. 8, no. Sistemnyj analiz, Upravlenie i obrabotka informacii, pp. 296–304, 2023, doi: 10.24412/2071-6168-2023-8-296-297.

James C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms.

J. C. Bezdek, “A Convergence Theorem for the Fuzzy ISODATA

Clustering Algorithms,” IEEE Trans Pattern Anal Mach Intell, vol. PAMI-2, no. 1, pp. 1–8, 1980, doi: 10.1109/TPAMI.1980.4766964.

T. T. Z. Nguen and L. V. Chernen'kaja, “Model' Prognozirovanija Jevristicheskih Nechetkih Vremennyh Rjadov Vysokogo Porjadka, Osnovannaja Na Hedzh-Algebraicheskom Podhode Chast' 3,” Zhurnal Izvestija Tul'skogo gosudarstvennogo universiteta – Tehnicheskie nauki (TulGU, g. Tula), vol. 9, no. Sistemnyj analiz, Upravlenie i obrabotka informacii, 2023.

Nguen Thi Thu Zung and L. V. Chernen'kaja, “Modeli dlja analiza razvitija jekonomiki neskol'kih rajonov v'etnama na osnove matematicheskih metodov mnogomernoj statistiki,” in V sbornike: Impul's organizacionnyh innovacij. Sbornik konkursnyh rabot 1 mezhvuzovskogo konkursa studentov, magistrantov i aspirantov, Moskva, 2020, pp. 301–324.

V. Garcia Domingo and J. M. Nuñez Do Rio Carles Ventura Royo, “Fuzzy C-means and clustering algorithms: a comparative study,” 2019.

Nguen Thi Thu Zung and Vasil'evna Chernen'kaja Ljudmila, “Model' Prognozirovanija Jevristicheskih Nechetkih Vremennyh Rjadov Vysokogo Porjadka, Osnovannaja Na Hedzh-Algebraicheskom Podhode Chast' 1,” Zhurnal Izvestija Tul'skogo gosudarstvennogo universiteta – Tehnicheskie nauki (TulGU, g. Tula), vol. 9, no. Sistemnyj analiz, Upravlenie i obrabotka informacii, 2023.

A. Jurio, D. Paternain, H. Bustince, C. Guerra, and G. Beliakov, “A construction method of Atanassov’s Intuitionistic fuzzy set for image processing,” in 5th IEEE International Conference Intelligent Systems, Institute of Electrical and Electronics Engineers, Ed., University of Westminster, London, UK: 2020 IEEE International Conference on Intelligent Systems , Jul. 2010, pp. 337–342.

K. Atanassov, “Type-1 Fuzzy Sets and Intuitionistic Fuzzy Sets,” Algorithms, vol. 10, no. 3, p. 106, 2017, doi: 10.3390/a10030106.

K. T. Atanassov, “Operators over interval valued intuitionistic fuzzy sets,” Fuzzy Sets Syst, vol. 64, no. 2, pp. 159–174, 1994, doi: 10.1016/0165-0114(94)90331-X.

K. T. Atanassov, “Two theorems for intuitionistic fuzzy sets,” Fuzzy Sets Syst, vol. 110, no. 2, pp. 267–269, 2000, doi: 10.1016/S0165-0114(99)00112-8.

K. T. Atanassov, “Review and new results on intuitionistic fuzzy sets,” International Journal Bioautomation, vol. 20, pp. S17–S26, 2016.

K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets Syst, vol. 20, pp. 87–96, 1986.

K. T. Atanassov, “More on intuitionistic fuzzy sets,” Fuzzy set and Systems, vol. 33, pp. 37–45, 1989.

K. Atanassov and G. Gargov, “Interval valued intuitionistic fuzzy sets,” Fuzzy Sets Syst, vol. 31, no. 3, pp. 343–349, 1989, doi: 10.1016/0165-0114(89)90205-4.

Z. Xu and J. Wu, “Intuitionistic fuzzy C-means clustering algorithms,” Journal of Systems Engineering and Electronics, vol. 21, no. 4, pp. 580–590, 2010, doi: 10.3969/j.issn.1004-4132.2010.04.009.

P. Kaur, A. K. Soni, and A. Gosain, “Novel intuitionistic fuzzy c-means clustering for linearly and nonlinearly separable data,” WSEAS Transactions on Computers, vol. 11, no. 3, pp. 65–76, 2012.

B. K. Tripathy, A. Basu, and S. Govel, “Image segmentation using spatial intuitionistic fuzzy C means clustering,” 2014 IEEE International Conference on Computational Intelligence and Computing Research, IEEE ICCIC 2014, no. September 2015, pp. 10–12, 2014, doi: 10.1109/ICCIC.2014.7238446.

X. Zhou, R. Zhao, F. Yu, and H. Tian, “Intuitionistic fuzzy entropy clustering algorithm for infrared image segmentation,” Journal of Intelligent and Fuzzy Systems, vol. 30, no. 3, pp. 1831–1840, 2016, doi: 10.3233/IFS-151894.

M. Kaushal, R. Solanki, Q. M. Danish Lohani, and P. K. Muhuri, “A novel intuitionistic fuzzy set generator with application to clustering,” IEEE International Conference on Fuzzy Systems, vol. 2018-July, pp. 1–8, 2018, doi: 10.1109/FUZZ-IEEE.2018.8491602.

H. Verma, A. Gupta, and D. Kumar, “A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree,” Pattern Recognit Lett, vol. 122, pp. 45–52, 2019, doi: 10.1016/j.patrec.2019.02.017.

O. Dogan, B. Oztaysi, and C. Fernandez-Llatas, “Segmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualization,” Journal of Intelligent and Fuzzy Systems, vol. 38, no. 1, pp. 675–684, 2020, doi: 10.3233/JIFS-179440.

M. Kaushal and Q. M. D. Lohani, “Generalized intuitionistic fuzzy c-means clustering algorithm using an adaptive intuitionistic fuzzification technique,” Granular Computing, vol. 7, no. 1, pp. 183–195, 2022, doi: 10.1007/s41066-021-00259-1.

Z. Xu and R. R. Yager, “Some geometric aggregation operators based on intuitionistic fuzzy sets,” Int J Gen Syst, vol. 35, no. 4, pp. 417–433, Aug. 2006, doi: 10.1080/03081070600574353.

M. Dhyani, G. S. Kushwaha, and S. Kumar, “A novel intuitionistic fuzzy inference system for sentiment analysis,” International Journal of Information Technology (Singapore), vol. 14, no. 6, pp. 3193–3200, Oct. 2022, doi: 10.1007/s41870-022-01014-8.

O. Castillo and P. Melin, “A new method for fuzzy inference in intuitionistic fuzzy systems,” Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, vol. 2003-Janua, pp. 20–25, 2003, doi: 10.1109/NAFIPS.2003.1226749.


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