FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy
The most common issues for many clustering algorithms include the slow convergence, requirement for pre-specification of a number of parameters, and the lack of robustness when dealing with anomalies. Recently, the density peak clustering (DPC) algorithm was proposed to discover the centers of clust...
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| Format: | Article |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8754775/ |
| _version_ | 1818665180608856064 |
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| author | Milan D. Parmar Wei Pang Dehao Hao Jianhua Jiang Wang Liupu Limin Wang You Zhou |
| author_facet | Milan D. Parmar Wei Pang Dehao Hao Jianhua Jiang Wang Liupu Limin Wang You Zhou |
| author_sort | Milan D. Parmar |
| collection | DOAJ |
| description | The most common issues for many clustering algorithms include the slow convergence, requirement for pre-specification of a number of parameters, and the lack of robustness when dealing with anomalies. Recently, the density peak clustering (DPC) algorithm was proposed to discover the centers of clusters by finding the density peaks in a dataset based on their local densities. The DPC needs neither an iterative process nor a large number of parameters, and it supports a heuristic approach, known as the decision graph, to manually select cluster centroids. However, the selection of the key parameters of the DPC was not systematically investigated. In this paper, we propose the feasible residual error-based density peak clustering algorithm with the fragment merging strategy, where the local density within the neighborhood region is measured through the residual error computation and the resulting residual errors are then used to generate residual fragments for cluster formation. The model parameters are then able to be calculated from the equations with statistical theoretical justification. We also develop a semi-automatic cluster identification method to eliminate the iterative process of manual centroid selection. The robustness and effectiveness of the proposed algorithm compared to the DPC and other clustering algorithms are demonstrated through experiments on standard benchmark datasets. The proposed method named feasible residual error-based density peak clustering (FREDPC) algorithm with the fragment merging strategy only needs to perform in one single step without any iteration and thus it is fast and has a great potential to be applied on a wide range of applications. |
| first_indexed | 2024-12-17T05:44:32Z |
| format | Article |
| id | doaj.art-317ffb735791448796dc319e37e5b2f1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:44:32Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-317ffb735791448796dc319e37e5b2f12022-12-21T22:01:21ZengIEEEIEEE Access2169-35362019-01-017897898980410.1109/ACCESS.2019.29265798754775FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging StrategyMilan D. Parmar0https://orcid.org/0000-0002-7596-407XWei Pang1Dehao Hao2Jianhua Jiang3https://orcid.org/0000-0002-9149-2922Wang Liupu4Limin Wang5You Zhou6https://orcid.org/0000-0003-0013-1281College of Computer Science and Technology, Jilin University, Changchun, ChinaDepartment of Computing Science, University of Aberdeen, Aberdeen, U.K.School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaThe most common issues for many clustering algorithms include the slow convergence, requirement for pre-specification of a number of parameters, and the lack of robustness when dealing with anomalies. Recently, the density peak clustering (DPC) algorithm was proposed to discover the centers of clusters by finding the density peaks in a dataset based on their local densities. The DPC needs neither an iterative process nor a large number of parameters, and it supports a heuristic approach, known as the decision graph, to manually select cluster centroids. However, the selection of the key parameters of the DPC was not systematically investigated. In this paper, we propose the feasible residual error-based density peak clustering algorithm with the fragment merging strategy, where the local density within the neighborhood region is measured through the residual error computation and the resulting residual errors are then used to generate residual fragments for cluster formation. The model parameters are then able to be calculated from the equations with statistical theoretical justification. We also develop a semi-automatic cluster identification method to eliminate the iterative process of manual centroid selection. The robustness and effectiveness of the proposed algorithm compared to the DPC and other clustering algorithms are demonstrated through experiments on standard benchmark datasets. The proposed method named feasible residual error-based density peak clustering (FREDPC) algorithm with the fragment merging strategy only needs to perform in one single step without any iteration and thus it is fast and has a great potential to be applied on a wide range of applications.https://ieeexplore.ieee.org/document/8754775/Clusteringdensity peak clusteringanomaly detectionresidual errorresidual fragment |
| spellingShingle | Milan D. Parmar Wei Pang Dehao Hao Jianhua Jiang Wang Liupu Limin Wang You Zhou FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy IEEE Access Clustering density peak clustering anomaly detection residual error residual fragment |
| title | FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy |
| title_full | FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy |
| title_fullStr | FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy |
| title_full_unstemmed | FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy |
| title_short | FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy |
| title_sort | fredpc a feasible residual error based density peak clustering algorithm with the fragment merging strategy |
| topic | Clustering density peak clustering anomaly detection residual error residual fragment |
| url | https://ieeexplore.ieee.org/document/8754775/ |
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