Relative density-based clustering algorithm for identifying diverse density clusters effectively
Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we propose a novel clustering algorithm relative density-based clustering algorithm for identifying diverse density clusters effectively called ID...
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| Vydané v: | Neural computing & applications Ročník 33; číslo 16; s. 10141 - 10157 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
Springer London
01.08.2021
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we propose a novel clustering algorithm relative density-based clustering algorithm for identifying diverse density clusters effectively called IDDC. It can effectively identify clusters in data sets with different densities and can also handle outliers. We first compute relative density for each data point. Then, the density peak points are screened and the initial clusters are obtained according to these peak points. The strategy for assigning the remaining points is to find unallocated points from the perspective of the cluster, which can effectively identify different density. In experiments, we compare the proposed algorithm IDDC with some existing algorithms on synthetic and real-world data sets. The results show that IDDC performs better than those existing algorithms, especially clustering on data set with uneven density distribution. |
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| AbstractList | Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we propose a novel clustering algorithm relative density-based clustering algorithm for identifying diverse density clusters effectively called IDDC. It can effectively identify clusters in data sets with different densities and can also handle outliers. We first compute relative density for each data point. Then, the density peak points are screened and the initial clusters are obtained according to these peak points. The strategy for assigning the remaining points is to find unallocated points from the perspective of the cluster, which can effectively identify different density. In experiments, we compare the proposed algorithm IDDC with some existing algorithms on synthetic and real-world data sets. The results show that IDDC performs better than those existing algorithms, especially clustering on data set with uneven density distribution. |
| Author | Yang, Youlong Wang, Yuying |
| Author_xml | – sequence: 1 givenname: Yuying orcidid: 0000-0001-8325-1814 surname: Wang fullname: Wang, Yuying email: wangyuying@stu.xidian.edu.cn organization: School of Mathematics and Statistics, Xidian University – sequence: 2 givenname: Youlong surname: Yang fullname: Yang, Youlong organization: School of Mathematics and Statistics, Xidian University |
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| Snippet | Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we... |
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| SubjectTerms | Algorithms Artificial Intelligence Clustering Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data mining Data Mining and Knowledge Discovery Data points Datasets Density Density distribution Image Processing and Computer Vision Original Article Outliers (statistics) Probability and Statistics in Computer Science |
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| Title | Relative density-based clustering algorithm for identifying diverse density clusters effectively |
| URI | https://link.springer.com/article/10.1007/s00521-021-05777-2 https://www.proquest.com/docview/2549482027 |
| Volume | 33 |
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