Abstract
Graph-based clustering is an efficient method for identifying clusters in local and nonlinear data patterns. Among the existing methods, spectral clustering is one of the most prominent algorithms. However, this method is vulnerable to noise and outliers. This study proposes a robust graph-based clustering method that removes the data nodes of relatively low density. The proposed method calculates the pseudo-density from a similarity matrix, and reconstructs it using a sparse regularization model. In this process, noise and the outer points are determined and removed. Unlike previous edge cutting-based methods, the proposed method is robust to noise while detecting clusters because it cuts out irrelevant nodes. We use a simulation and real-world data to demonstrate the usefulness of the proposed method by comparing it to existing methods in terms of clustering accuracy and robustness to noisy data. The comparison results confirm that the proposed method outperforms the alternatives.
Original language | English |
---|---|
Article number | 107001 |
Journal | Pattern Recognition |
Volume | 97 |
DOIs | |
Publication status | Published - 2020 Jan |
Bibliographical note
Funding Information:The authors would like to thank the editor and reviewers for their useful comments and suggestions, which were of great help in improving the quality of the paper. This research was supported by Brain Korea PLUS ; the Basic Science Research Program through the National Research Foundation of Korea, funded by the M inistry of Science, ICT and Future Planning ( NRF-2016R1A2B1008994 ); the Ministry of Trade, Industry & Energy under the Industrial Technology Innovation Program ( R1623371 ), and by an Institute for Information & communications Technology Promotion grant funded by the Korea government (No. 2018-0-00440 , ICT-based Crime Risk Prediction and Response Platform Development for Early Awareness of Risk Situation).
Funding Information:
The authors would like to thank the editor and reviewers for their useful comments and suggestions, which were of great help in improving the quality of the paper. This research was supported by Brain Korea PLUS; the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Science, ICT and Future Planning (NRF-2016R1A2B1008994); the Ministry of Trade, Industry & Energy under the Industrial Technology Innovation Program (R1623371), and by an Institute for Information & communications Technology Promotion grant funded by the Korea government (No. 2018-0-00440, ICT-based Crime Risk Prediction and Response Platform Development for Early Awareness of Risk Situation).
Publisher Copyright:
© 2019
Keywords
- Graph-based clustering
- Node cutting
- Pseudo-density reconstruction
- Spectral clustering
- Unsupervised learning
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence