Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood Graph
Density-based clustering methods have achieved many applications in data mining, whereas most of them still likely suffer poor performances on data sets with extremely uneven distributions, like the manifold or ring data. The paper proposes a novel method for clustering with local peaks in the symme...
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doaj-12f07d8723ad4a88a9af2bb84632ca842021-03-30T01:12:14ZengIEEEIEEE Access2169-35362020-01-0181600161210.1109/ACCESS.2019.29623948943372Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood GraphZhi Liu0https://orcid.org/0000-0001-6972-2464Chunrong Wu1https://orcid.org/0000-0002-2691-093XQinglan Peng2https://orcid.org/0000-0002-8908-5201Jia Lee3https://orcid.org/0000-0002-2304-4263Yunni Xia4https://orcid.org/0000-0001-9024-732XSchool of Architecture and Urban Planning, Chongqing University, Chongqing, ChinaCollege of Computer Science, Chongqing University, Chongqing, ChinaCollege of Computer Science, Chongqing University, Chongqing, ChinaCollege of Computer Science, Chongqing University, Chongqing, ChinaCollege of Computer Science, Chongqing University, Chongqing, ChinaDensity-based clustering methods have achieved many applications in data mining, whereas most of them still likely suffer poor performances on data sets with extremely uneven distributions, like the manifold or ring data. The paper proposes a novel method for clustering with local peaks in the symmetric neighborhood. Local peaks are points with maximum densities at the local level. During the searching of local peaks, all data, except those outliers, can be easily divided into a number of small clusters in accordance with the local peaks in each point's neighborhood. Especially, a graph-based scheme is adopted here to merge similar clusters based on their similarity in the symmetric neighborhood graph, followed by assigning each outlier to the closest cluster. A variety of artificial, real data sets and a real building data set have been tested for clustering by the proposed method and compared against other popular density-based methods and other algorithms.https://ieeexplore.ieee.org/document/8943372/Data miningclusteringdensity-based clusteringsymmetric neighborhoodlocal peaks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhi Liu Chunrong Wu Qinglan Peng Jia Lee Yunni Xia |
spellingShingle |
Zhi Liu Chunrong Wu Qinglan Peng Jia Lee Yunni Xia Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood Graph IEEE Access Data mining clustering density-based clustering symmetric neighborhood local peaks |
author_facet |
Zhi Liu Chunrong Wu Qinglan Peng Jia Lee Yunni Xia |
author_sort |
Zhi Liu |
title |
Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood Graph |
title_short |
Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood Graph |
title_full |
Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood Graph |
title_fullStr |
Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood Graph |
title_full_unstemmed |
Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood Graph |
title_sort |
local peaks-based clustering algorithm in symmetric neighborhood graph |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Density-based clustering methods have achieved many applications in data mining, whereas most of them still likely suffer poor performances on data sets with extremely uneven distributions, like the manifold or ring data. The paper proposes a novel method for clustering with local peaks in the symmetric neighborhood. Local peaks are points with maximum densities at the local level. During the searching of local peaks, all data, except those outliers, can be easily divided into a number of small clusters in accordance with the local peaks in each point's neighborhood. Especially, a graph-based scheme is adopted here to merge similar clusters based on their similarity in the symmetric neighborhood graph, followed by assigning each outlier to the closest cluster. A variety of artificial, real data sets and a real building data set have been tested for clustering by the proposed method and compared against other popular density-based methods and other algorithms. |
topic |
Data mining clustering density-based clustering symmetric neighborhood local peaks |
url |
https://ieeexplore.ieee.org/document/8943372/ |
work_keys_str_mv |
AT zhiliu localpeaksbasedclusteringalgorithminsymmetricneighborhoodgraph AT chunrongwu localpeaksbasedclusteringalgorithminsymmetricneighborhoodgraph AT qinglanpeng localpeaksbasedclusteringalgorithminsymmetricneighborhoodgraph AT jialee localpeaksbasedclusteringalgorithminsymmetricneighborhoodgraph AT yunnixia localpeaksbasedclusteringalgorithminsymmetricneighborhoodgraph |
_version_ |
1724187449026936832 |