KR-DBSCAN: A density-based clustering algorithm based on reverse nearest neighbor and influence space
Density-based clustering is one of the most commonly used analysis methods in data mining and machine learning, with the advantage of locating non-ball-shaped clusters without specifying the number of clusters in advance. However, it has notable shortcomings, such as an inability to distinguish adja...
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| Published in: | Expert systems with applications Vol. 186; p. 115763 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
30.12.2021
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | Density-based clustering is one of the most commonly used analysis methods in data mining and machine learning, with the advantage of locating non-ball-shaped clusters without specifying the number of clusters in advance. However, it has notable shortcomings, such as an inability to distinguish adjacent clusters of different densities. We propose a density-based clustering algorithm, KR-DBSCAN, which is based on the reverse nearest neighbor and influence space. The core objects are identified according to the reverse nearest neighborhood, and their influence spaces are determined by calculating the k-nearest neighborhood and reverse nearest neighborhood for each data object under the Euclidean distance metric. In particular, a new cluster expansion condition is defined using the reverse nearest neighborhood and its influence space, and when the core objects are within their influence spaces, they are added to the cluster by breadth-first traversal. As a result, adjacent clusters with different densities are effectively distinguished, and the computational load is substantially reduced. Boundary objects and noise objects are identified, also using k-nearest neighbors. KR-DBSCAN is experimentally validated on the UCI dataset and some synthetic datasets.
•We define a new cluster expanding condition for the density-based clustering.•We design a new noise removal approach in the density-based clustering analysis.•We propose a density-based clustering algorithm KR-DBSCAN.•KR-DBSCAN is evaluated through extensive experiments. |
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| AbstractList | Density-based clustering is one of the most commonly used analysis methods in data mining and machine learning, with the advantage of locating non-ball-shaped clusters without specifying the number of clusters in advance. However, it has notable shortcomings, such as an inability to distinguish adjacent clusters of different densities. We propose a density-based clustering algorithm, KR-DBSCAN, which is based on the reverse nearest neighbor and influence space. The core objects are identified according to the reverse nearest neighborhood, and their influence spaces are determined by calculating the k-nearest neighborhood and reverse nearest neighborhood for each data object under the Euclidean distance metric. In particular, a new cluster expansion condition is defined using the reverse nearest neighborhood and its influence space, and when the core objects are within their influence spaces, they are added to the cluster by breadth-first traversal. As a result, adjacent clusters with different densities are effectively distinguished, and the computational load is substantially reduced. Boundary objects and noise objects are identified, also using k-nearest neighbors. KR-DBSCAN is experimentally validated on the UCI dataset and some synthetic datasets.
•We define a new cluster expanding condition for the density-based clustering.•We design a new noise removal approach in the density-based clustering analysis.•We propose a density-based clustering algorithm KR-DBSCAN.•KR-DBSCAN is evaluated through extensive experiments. Density-based clustering is one of the most commonly used analysis methods in data mining and machine learning, with the advantage of locating non-ball-shaped clusters without specifying the number of clusters in advance. However, it has notable shortcomings, such as an inability to distinguish adjacent clusters of different densities. We propose a density-based clustering algorithm, KR-DBSCAN, which is based on the reverse nearest neighbor and influence space. The core objects are identified according to the reverse nearest neighborhood, and their influence spaces are determined by calculating the k-nearest neighborhood and reverse nearest neighborhood for each data object under the Euclidean distance metric. In particular, a new cluster expansion condition is defined using the reverse nearest neighborhood and its influence space, and when the core objects are within their influence spaces, they are added to the cluster by breadth-first traversal. As a result, adjacent clusters with different densities are effectively distinguished, and the computational load is substantially reduced. Boundary objects and noise objects are identified, also using k-nearest neighbors. KR-DBSCAN is experimentally validated on the UCI dataset and some synthetic datasets. |
| ArticleNumber | 115763 |
| Author | Hu, Lihua Liu, Aiqin Liu, Hongkai Zhang, Jifu |
| Author_xml | – sequence: 1 givenname: Lihua surname: Hu fullname: Hu, Lihua email: sxtyhlh@126.com – sequence: 2 givenname: Hongkai surname: Liu fullname: Liu, Hongkai email: liuhongkai0422@qq.com – sequence: 3 givenname: Jifu orcidid: 0000-0002-0396-8901 surname: Zhang fullname: Zhang, Jifu email: jifuzh@sina.com – sequence: 4 givenname: Aiqin surname: Liu fullname: Liu, Aiqin email: 103125260@qq.com |
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| Cites_doi | 10.1016/j.oceaneng.2018.12.019 10.1016/j.eswa.2019.01.046 10.1016/j.engappai.2018.09.012 10.1109/TC.2018.2879332 10.1007/s11277-017-5044-z 10.1109/TIT.1967.1053964 10.1016/j.neucom.2015.05.109 10.1186/s12918-019-0690-2 10.1016/j.eswa.2019.03.031 10.1016/j.is.2012.09.001 10.1088/1402-4896/ab0a9f 10.1007/s11222-007-9033-z 10.1016/j.neucom.2018.06.087 10.1109/TKDE.2017.2787640 10.1007/s11704-013-3158-3 10.1016/j.apr.2018.06.006 10.1145/3068335 10.1002/widm.30 10.1016/j.eswa.2019.02.030 10.1126/science.1242072 |
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| Keywords | Reverse nearest neighborhood Influence space Cluster expansion Core object Density-based clustering |
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| SubjectTerms | Algorithms Cluster expansion Clustering Core object Data mining Datasets Density-based clustering Euclidean geometry Influence space Machine learning Neighborhoods Reverse nearest neighborhood Space density |
| Title | KR-DBSCAN: A density-based clustering algorithm based on reverse nearest neighbor and influence space |
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