DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores
Numerous clustering algorithms become invalid when data classes are unbalanced and close to each other, or clusters are arbitrary shapes and have different densities. A novel region density based clustering algorithm, DEDIC, is proposed to meet this challenge. It is based on recognizing dense region...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9987490/ |
| _version_ | 1797978273046593536 |
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| author | Yisen Lin Xinlun Zhang Lei Liu Huichen Qu |
| author_facet | Yisen Lin Xinlun Zhang Lei Liu Huichen Qu |
| author_sort | Yisen Lin |
| collection | DOAJ |
| description | Numerous clustering algorithms become invalid when data classes are unbalanced and close to each other, or clusters are arbitrary shapes and have different densities. A novel region density based clustering algorithm, DEDIC, is proposed to meet this challenge. It is based on recognizing dense regions using mutual nearest neighbors and estimating region density of a cluster by computing maximum of the directly-reachable distances among core points within the cluster. DEDIC is superior to the popular density-based clustering algorithm DBSCAN in two aspects. First, since there is only one parameter (choice of k nearest neighbors), the algorithm complexity is reduced, and second, an improved ability to handle clusters with large density variations. The superiority of DEDIC is demonstrated on several artificial and real-world datasets with respect to nine known clustering approaches. |
| first_indexed | 2024-04-11T05:21:29Z |
| format | Article |
| id | doaj.art-36e2a9a6e06147908f9af2efb2080be5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T05:21:29Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-36e2a9a6e06147908f9af2efb2080be52022-12-24T00:00:52ZengIEEEIEEE Access2169-35362022-01-011013203113203910.1109/ACCESS.2022.32295829987490DEDIC: Density Estimation Clustering Method Using Directly Interconnected CoresYisen Lin0https://orcid.org/0000-0001-7037-767XXinlun Zhang1Lei Liu2Huichen Qu3School of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, ChinaSchool of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, ChinaSchool of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, ChinaSchool of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, ChinaNumerous clustering algorithms become invalid when data classes are unbalanced and close to each other, or clusters are arbitrary shapes and have different densities. A novel region density based clustering algorithm, DEDIC, is proposed to meet this challenge. It is based on recognizing dense regions using mutual nearest neighbors and estimating region density of a cluster by computing maximum of the directly-reachable distances among core points within the cluster. DEDIC is superior to the popular density-based clustering algorithm DBSCAN in two aspects. First, since there is only one parameter (choice of k nearest neighbors), the algorithm complexity is reduced, and second, an improved ability to handle clusters with large density variations. The superiority of DEDIC is demonstrated on several artificial and real-world datasets with respect to nine known clustering approaches.https://ieeexplore.ieee.org/document/9987490/Clusteringregion densitymutual neighborsdimensionality reductionarbitrary shapespattern recognition |
| spellingShingle | Yisen Lin Xinlun Zhang Lei Liu Huichen Qu DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores IEEE Access Clustering region density mutual neighbors dimensionality reduction arbitrary shapes pattern recognition |
| title | DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores |
| title_full | DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores |
| title_fullStr | DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores |
| title_full_unstemmed | DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores |
| title_short | DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores |
| title_sort | dedic density estimation clustering method using directly interconnected cores |
| topic | Clustering region density mutual neighbors dimensionality reduction arbitrary shapes pattern recognition |
| url | https://ieeexplore.ieee.org/document/9987490/ |
| work_keys_str_mv | AT yisenlin dedicdensityestimationclusteringmethodusingdirectlyinterconnectedcores AT xinlunzhang dedicdensityestimationclusteringmethodusingdirectlyinterconnectedcores AT leiliu dedicdensityestimationclusteringmethodusingdirectlyinterconnectedcores AT huichenqu dedicdensityestimationclusteringmethodusingdirectlyinterconnectedcores |