• Tran Manh Tuan Faculty of Computer Science and Engineering, Thuyloi University, Viet Nam
  • Phung The Huan Thai Nguyen University, University of Information and Communication Technology
  • Pham Huy Thong VNU Information Technology Institute, Vietnam National University
  • Tran Thi Ngan Faculty of Computer Science and Engineering, Thuyloi University, Viet Nam
  • Le Hoang Son VNU Information Technology Institute, Vietnam National University



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


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