Iterative Min Cut Clustering Based on Graph Cuts
1
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 474; https://doi.org/10.3390/s21020474
Received: 4 December 2020
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Revised: 5 January 2021
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Accepted: 5 January 2021
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Published: 11 January 2021
(This article belongs to the Special Issue Intelligent Sensors and Machine Learning)
Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time.
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Keywords:
clustering; graph cuts; variational method; partial differential equation; nonlinearly separable datasets
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MDPI and ACS Style
Liu, B.; Liu, Z.; Li, Y.; Zhang, T.; Zhang, Z. Iterative Min Cut Clustering Based on Graph Cuts. Sensors 2021, 21, 474. https://doi.org/10.3390/s21020474
AMA Style
Liu B, Liu Z, Li Y, Zhang T, Zhang Z. Iterative Min Cut Clustering Based on Graph Cuts. Sensors. 2021; 21(2):474. https://doi.org/10.3390/s21020474
Chicago/Turabian StyleLiu, Bowen, Zhaoying Liu, Yujian Li, Ting Zhang, and Zhilin Zhang. 2021. "Iterative Min Cut Clustering Based on Graph Cuts" Sensors 21, no. 2: 474. https://doi.org/10.3390/s21020474
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