AN IMPROVEMENT OF TRUSTED SAFE SEMI-SUPERVISED FUZZY CLUSTERING METHOD WITH MULTIPLE FUZZIFIERS
Keywords:Fuzzy clustering, Semi-supervised fuzzy clustering , Safe semi-supervised fuzzy clustering , Multiple fuzzifiers.
Data clustering are applied in various fields such as document classification, dental X-ray image segmentation, medical image segmentation, etc. Especially, clustering algorithms are used in satellite image processing in many important application areas, including classification of vehicles participating in traffic, logistics, classification of satellite images to forecast droughts, floods, forest fire, etc. In the process of collecting satellite image data, there are a number of factors such as clouds, weather, ... that can affect to image quality. Images with low quality will make the performance of clustering algorithms decrease. Apart from that, the parameter of fuzzification in clustering algorithms also affects to clustering results. In the past, clustering methods often used the same fuzzification parameter, m = 2. But in practice, each element should have its own parameter m. Therefore, determining the parameters m is necessary to increase fuzzy clustering performance. In this research, an improvement algorithm for the data partition with confidence problem and multi fuzzifier named as TS3MFCM is introduced. The proposed method consists of three steps namely as “FCM for labeled data”, “Data transformation”, and “Semi-supervised fuzzy clustering with multiple point fuzzifiers”. The proposed TS3MFCM method is implemented and experimentally compared against with the Confidence-weighted Safe Semi-Supervised Clustering (CS3FCM). The performance of proposed method is better than selected methods in both computational time and clustering accuracy on the same datasets
 J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, 1981.
 Salem Saleh Al-amri, N.V. Kalyankar, and S.D. Khamitkar, “Image segmentation by using thershod techniques,” Journal of Computing, vol. 2, no. 5, 2010, pp. 83–86.
 Bezdek, James C., Robert Ehrlich, and William Full, “FCM: The fuzzy c-means clustering algorithm,” Computers & Geosciences, vol. 10, no. 2–3, 1984, pp. 191–203. https://doi.org/10.1016/0098-3004(84)90020-7
 T. C. Havens, J. C. Bezdek, C. Leckie, L. O. Hall and M. Palaniswami, “Fuzzy c-Means Algorithms for Very Large Data,” in IEEE Transactions on Fuzzy Systems, vol. 20, no. 6, pp. 1130–1146, Dec. 2012. Doi: 10.1109/TFUZZ.2012.2201485
 Seresht, N. G., Lourenzutti, R., & Fayek, A. R. (2020). A fuzzy clustering algorithm for developing predictive models in construction applications. Applied Soft Computing,96, 106679.
 H. Lu, S. Liu, H. Wei, and J. Tu, “Multi-kernel fuzzy clustering based on auto-encoder for fMRI functional network,” Expert Systems with Applications, vol. 159, November 2020, https://doi.org/10.1016/j.eswa.2020.113513
 Q. T. Bui, B. Vo, V. Snasel, W. Pedrycz, T. P. Hong, N. T. Nguyen, and M. Y. Chen, “SFCM: A fuzzy clustering algorithm of extracting the shape information of data,” in IEEE Transactions on Fuzzy Systems, vol. 29, no. 1, pp. 75–89, Jan. 2021, Doi: 10.1109/TFUZZ.2020.3014662
 H. Li, and M. Wei, “Fuzzy clustering based on feature weights for multivariate time series,” Knowledge-Based Systems, vol. 197, 7 June 2020, 105907, https://doi.org/10.1016/j.knosys.2020.105907
 W. Pedrycz and J.Waletzky, “Fuzzy clustering with partial supervision,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 27, no. 5, pp. 787-795, Oct. 1997, Doi: 10.1109/3477.623232
 S. Kundu, U. Maulik, and A. Mukhopadhyay, “A game theory-based approach to fuzzy clustering for pixel classification in remote sensing imagery,” Soft Comput, vol. 25, 5121–5129, 2021, https://doi.org/10.1007/s00500-020-05514-2
 F. Salehi, M. R. Keyvanpour, and A. Sharifi, “SMKFC-ER: Semi-supervised multiple kernel fuzzy clustering based on entropy and relative entropy,” Information Sciences, vol. 547, 667–688, 2021, https://doi.org/10.1016/j.ins.2020.08.094
 J. Xiong, X. Liu, X. Zhu, H. Zhu, H. Li and Q. Zhang, “Semi-supervised fuzzy c-means clustering optimized by simulated annealing and genetic algorithm for fault diagnosis of bearings,” IEEE Access, vol. 8, pp. 181976-181987, 2020, Doi: 10.1109/ACCESS.2020.3021720
 H. Gan, Y. Fan, Z. Luo, R. Huang, and Z. Yang, “Confidence-weighted safe semi-supervised clustering,” Engineering Applications of Artificial Intelligence, vol. 81, pp. 107-116, May 2019, https://doi.org/10.1016/j.engappai.2019.02.007
 S.D. Mai, and L.T. Ngo, “Multiple kernel approach to semi-supervised fuzzy clustering algorithm for land-cover classification,” Engineering Applications of Artificial Intelligence, vol. 68, pp. 205-213, February 2018, https://doi.org/10.1016/j.engappai.2017.11.007
 O. Komori, S. Eguchi, “A unified formulation of k-Means, fuzzy c-Means and Gaussian mixture model by the Kolmogorov–Nagumo average,” Entropy, vol. 23, no. 5, 2021, https://doi.org/10.3390/e23050518
 L.H. Son, T.M. Tuan, “Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints,” Engineering Applications of Artificial Intelligence, vol. 59, pp. 186-195, March 2017, https://doi.org/10.1016/j.engappai.2017.01.003
 B. Li, X. Xie, X.Wei, and W. Tang, “Ship detection and classification from optical remote sensing
images: A survey,” Chinese Journal of Aeronautics, vol. 34, no. 3, pp. 145-163, March 2021, https://doi.org/10.1016/j.cja.2020.09.022
 G. Casalino, G. Castellano, C. Mencar, “Data stream classification by dynamic incremental semi-supervised fuzzy clustering,” International Journal on Artificial Intelligence Tools, vol. 28, no. 8, 2019, https://doi.org/10.1142/S0218213019600091
 T. D. Khang, N. D. Vuong, M. K. Tran, and M. Fowler, “Fuzzy C-means clustering algorithm with multiple fuzzifier,” Algorithms, vol. 13, no. 7, 2020, https://doi.org/10.3390/a13070158
 H. Gan, “Safe semi-supervised fuzzy c -Means clustering,” IEEE Access, vol. 7, pp. 95659-95664, 2019, Doi: 10.1109/ACCESS.2019.2929307
 H. Gan, Y. Fan, Z. Luo, and Q. Zhang, “Local homogeneous consistent safe semisupervised clustering,” Expert Systems with Applications, vol. 97, pp. 384-393, 2018, https://doi.org/10.1016/j.eswa.2017.12.046
 L. Lov´asz, M. D. Plummer, Matching theory, vol. 367, Ams Chelsea Publishing, 2009.
 Outlier Detection DataSets (2021). Data. Online: http://odds.cs.stonybrook.edu/
 Satellite Image DataSets of Ships (2018). Data. Online: https://www.kaggle.com/c/airbus-ship-detection/
 C. Hwang and F. C. Rhee, “Uncertain fuzzy clustering: Interval type-2 fuzzy approach to c-Means,” in IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 107-120, Feb. 2007, Doi:10.1109/TFUZZ.2006.889763
 L. Vendramin, R. J. Campello, and E. R. Hruschka, “Relative clustering validity criteria: A comparative overview,” Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 3, no. 4, pp. 209-235, 2010, https://doi.org/10.1002/sam.10080
License1. We hereby assign copyright of our article (the Work) in all forms of media, whether now known or hereafter developed, to the Journal of Computer Science and Cybernetics. We understand that the Journal of Computer Science and Cybernetics will act on my/our behalf to publish, reproduce, distribute and transmit the Work.
2. This assignment of copyright to the Journal of Computer Science and Cybernetics is done so on the understanding that permission from the Journal of Computer Science and Cybernetics is not required for me/us to reproduce, republish or distribute copies of the Work in whole or in part. We will ensure that all such copies carry a notice of copyright ownership and reference to the original journal publication.
3. We warrant that the Work is our results and has not been published before in its current or a substantially similar form and is not under consideration for another publication, does not contain any unlawful statements and does not infringe any existing copyright.
4. We also warrant that We have obtained the necessary permission from the copyright holder/s to reproduce in the article any materials including tables, diagrams or photographs not owned by me/us.