Iterative Min Cut Clustering Based on Graph Cuts

Iterative Min Cut Clustering Based on Graph Cuts

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...

Full description

Bibliographic Details
Main Authors: Bowen Liu, Zhaoying Liu, Yujian Li, Ting Zhang, Zhilin Zhang
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Sensors
Subjects:
clustering
graph cuts
variational method
partial differential equation
nonlinearly separable datasets
Online Access:https://www.mdpi.com/1424-8220/21/2/474
Description
Summary: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.
ISSN:1424-8220

Similar Items