VDPC: variational density peak clustering algorithm

The widely applied density peak clustering (DPC) algorithm makes an intuitive cluster formation assumption that cluster centers are often surrounded by data points with lower local density and far away from other data points with higher local density. However, this assumption suffers from one limita...

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Main Authors: Wang, Yizhang, Wang, Di, Zhou, You, Zhang, Xiaofeng, Quek, Chai
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/169128
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1691282023-07-03T02:40:49Z VDPC: variational density peak clustering algorithm Wang, Yizhang Wang, Di Zhou, You Zhang, Xiaofeng Quek, Chai School of Computer Science and Engineering Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Engineering::Computer science and engineering Density Peak Clustering Local Density Analysis The widely applied density peak clustering (DPC) algorithm makes an intuitive cluster formation assumption that cluster centers are often surrounded by data points with lower local density and far away from other data points with higher local density. However, this assumption suffers from one limitation that it is often problematic when identifying clusters with lower density because they might be easily merged into other clusters with higher density. As a result, DPC may not be able to identify clusters with variational density. To address this issue, we propose a variational density peak clustering (VDPC) algorithm, which is designed to systematically and autonomously perform the clustering task on datasets with various types of density distributions. Specifically, we first propose a novel method to identify the representatives among all data points and construct initial clusters based on the identified representatives for further analysis of the clusters’ property. Furthermore, we divide all data points into different levels according to their local density and propose a unified clustering framework by combining the advantages of both DPC and DBSCAN. Thus, all the identified initial clusters spreading across different density levels are systematically processed to form the final clusters. To evaluate the effectiveness of the proposed VDPC algorithm, we conduct extensive experiments using 20 datasets including eight synthetic, six real-world, and six image datasets. The experimental results show that VDPC outperforms two classical algorithms (i.e., DPC and DBSCAN) and four state-of-the-art extended DPC algorithms. This research is supported by the China Postdoctoral Science Foundation (Grant No. 2022M713064), Lvyangjinfeng Excellent Doctoral Program of Yangzhou (Grant No. YZLYJFJH2021YXBS105), Innovation and Entrepreneurship Program of Jiangsu Province (Grant No. JSSCBS20211048), and Jiangsu Provincial Universities of Natural Science General Program (Grant No. 21KJB520021). This research is also supported in part by the Shenzhen Science and Technology Program under Grant No. JCYJ20200109113201726 and the National Natural Science Foundation of China under Grant No. 61872108. This research is also supported by the National Key Research and Development Program of China (Grant No. 2021YFF1201200) and Science and Technology Development Foundation of Jilin Province (Grant No. 20200201300JC). 2023-07-03T02:40:49Z 2023-07-03T02:40:49Z 2023 Journal Article Wang, Y., Wang, D., Zhou, Y., Zhang, X. & Quek, C. (2023). VDPC: variational density peak clustering algorithm. Information Sciences, 621, 627-651. https://dx.doi.org/10.1016/j.ins.2022.11.091 0020-0255 https://hdl.handle.net/10356/169128 10.1016/j.ins.2022.11.091 2-s2.0-85145567645 621 627 651 en Information Sciences © 2022 Elsevier Inc. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Density Peak Clustering
Local Density Analysis
spellingShingle Engineering::Computer science and engineering
Density Peak Clustering
Local Density Analysis
Wang, Yizhang
Wang, Di
Zhou, You
Zhang, Xiaofeng
Quek, Chai
VDPC: variational density peak clustering algorithm
description The widely applied density peak clustering (DPC) algorithm makes an intuitive cluster formation assumption that cluster centers are often surrounded by data points with lower local density and far away from other data points with higher local density. However, this assumption suffers from one limitation that it is often problematic when identifying clusters with lower density because they might be easily merged into other clusters with higher density. As a result, DPC may not be able to identify clusters with variational density. To address this issue, we propose a variational density peak clustering (VDPC) algorithm, which is designed to systematically and autonomously perform the clustering task on datasets with various types of density distributions. Specifically, we first propose a novel method to identify the representatives among all data points and construct initial clusters based on the identified representatives for further analysis of the clusters’ property. Furthermore, we divide all data points into different levels according to their local density and propose a unified clustering framework by combining the advantages of both DPC and DBSCAN. Thus, all the identified initial clusters spreading across different density levels are systematically processed to form the final clusters. To evaluate the effectiveness of the proposed VDPC algorithm, we conduct extensive experiments using 20 datasets including eight synthetic, six real-world, and six image datasets. The experimental results show that VDPC outperforms two classical algorithms (i.e., DPC and DBSCAN) and four state-of-the-art extended DPC algorithms.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Wang, Yizhang
Wang, Di
Zhou, You
Zhang, Xiaofeng
Quek, Chai
format Article
author Wang, Yizhang
Wang, Di
Zhou, You
Zhang, Xiaofeng
Quek, Chai
author_sort Wang, Yizhang
title VDPC: variational density peak clustering algorithm
title_short VDPC: variational density peak clustering algorithm
title_full VDPC: variational density peak clustering algorithm
title_fullStr VDPC: variational density peak clustering algorithm
title_full_unstemmed VDPC: variational density peak clustering algorithm
title_sort vdpc: variational density peak clustering algorithm
publishDate 2023
url https://hdl.handle.net/10356/169128
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