A fast density peaks clustering algorithm with sparse search
Given a large unlabeled set of complex data, how to efficiently and effectively group them into clusters remains a challenging problem. Density peaks clustering (DPC) algorithm is an emerging algorithm, which identifies cluster centers based on a decision graph. Without setting the number of cluster...
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| Published in: | Information sciences Vol. 554; pp. 61 - 83 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
01.04.2021
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | Given a large unlabeled set of complex data, how to efficiently and effectively group them into clusters remains a challenging problem. Density peaks clustering (DPC) algorithm is an emerging algorithm, which identifies cluster centers based on a decision graph. Without setting the number of cluster centers, DPC can effectively recognize the clusters. However, the similarity between every two data points must be calculated to construct a decision graph, which results in high computational complexity. To overcome this issue, we propose a fast sparse search density peaks clustering (FSDPC) algorithm to enhance the DPC, which constructs a decision graph with fewer similarity calculations to identify cluster centers quickly. In FSDPC, we design a novel sparse search strategy to measure the similarity between the nearest neighbors of each data points. Therefore, FSDPC can enhance the efficiency of the DPC while maintaining satisfactory results. We also propose a novel random third-party data point method to search the nearest neighbors, which introduces no additional parameters or high computational complexity. The experimental results on synthetic datasets and real-world datasets indicate that the proposed algorithm consistently outperforms the DPC and other state-of-the-art algorithms. |
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| AbstractList | Given a large unlabeled set of complex data, how to efficiently and effectively group them into clusters remains a challenging problem. Density peaks clustering (DPC) algorithm is an emerging algorithm, which identifies cluster centers based on a decision graph. Without setting the number of cluster centers, DPC can effectively recognize the clusters. However, the similarity between every two data points must be calculated to construct a decision graph, which results in high computational complexity. To overcome this issue, we propose a fast sparse search density peaks clustering (FSDPC) algorithm to enhance the DPC, which constructs a decision graph with fewer similarity calculations to identify cluster centers quickly. In FSDPC, we design a novel sparse search strategy to measure the similarity between the nearest neighbors of each data points. Therefore, FSDPC can enhance the efficiency of the DPC while maintaining satisfactory results. We also propose a novel random third-party data point method to search the nearest neighbors, which introduces no additional parameters or high computational complexity. The experimental results on synthetic datasets and real-world datasets indicate that the proposed algorithm consistently outperforms the DPC and other state-of-the-art algorithms. |
| Author | Jia, Weikuan Xu, Xiao Ding, Shifei Wang, Yanru Wang, Lijuan |
| Author_xml | – sequence: 1 givenname: Xiao surname: Xu fullname: Xu, Xiao organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 2 givenname: Shifei surname: Ding fullname: Ding, Shifei email: dingsf@cumt.edu.cn organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 3 givenname: Yanru surname: Wang fullname: Wang, Yanru organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 4 givenname: Lijuan surname: Wang fullname: Wang, Lijuan organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 5 givenname: Weikuan surname: Jia fullname: Jia, Weikuan organization: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China |
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