Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

作     者：Pham Huy Thong Florentin Smarandache Phung The Huan Tran Manh Tuan Tran Thi Ngan Vu Duc Thai Nguyen Long Giang Le Hoang Son 

作者机构：Graduate University of Science and TechnologyVietnam Academy of Science and TechnologyHanoi100000Vietnam Institute of Information TechnologyVietnam Academy of Science and TechnologyHanoi100000Vietnam VNU Information Technology InstituteVietnam National UniversityHanoi100000Vietnam Department of MathematicsUniversity of New MexicoGallup87301New MexicoUSA University of Information and Communication TechnologyThai Nguyen UniversityThai Nguyen250000Vietnam Faculty of Computer Science and EngineeringThuyloi UniversityHanoi100000Vietnam 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第8期

页      面：1981-1997页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research is funded by Graduate University of Science and Technology under grant number GUST.STS.DT2020-TT01 

主　　题：Safe semi-supervised fuzzy clustering picture fuzzy set neutrosophic set data partition with noises fuzzy clustering 

摘      要：Clustering is a crucial method for deciphering data structure and producing new *** to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive *** with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering *** data can lead to incorrect object recognition and *** research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled *** effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI *** experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.