A novel density peak clustering algorithm based on squared residual error

机译：一种基于平方残差的密度峰值聚类算法

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摘要

The density peak clustering (DPC) algorithm is designed to quickly identify intricate-shaped clusters with high dimensionality by finding high-density peaks in a non-iterative manner and using only one threshold parameter. However, DPC has certain limitations in processing low-density data points because it only takes the global data density distribution into account. As such, DPC may confine in forming low-density data clusters, or in other words, DPC may fail in detecting anomalies and borderline points. In this paper, we analyze the limitations of DPC and propose a novel density peak clustering algorithm to better handle low-density clustering tasks. Specifically, our algorithm provides a better decision graph comparing to DPC for the determination of cluster centroids. Experimental results show that our algorithm outperforms DPC and other clustering algorithms on the benchmarking datasets.

机译：密度峰值聚类（DPC）算法旨在通过以非迭代方式查找高密度峰值并仅使用一个阈值参数来快速识别具有高维的复杂形状的聚类。但是，DPC在处理低密度数据点时有某些限制，因为它仅考虑了全局数据密度分布。这样，DPC可能会限制形成低密度数据簇，换句话说，DPC可能无法检测到异常和边界点。在本文中，我们分析了DPC的局限性，并提出了一种新的密度峰聚类算法，以更好地处理低密度聚类任务。具体而言，与DPC相比，我们的算法为确定聚类质心提供了更好的决策图。实验结果表明，在基准数据集上，我们的算法优于DPC和其他聚类算法。

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《2017 International Conference on Security, Pattern Analysis, and Cybernetics》|2017年|43-48|共6页
会议地点 Shenzhen(CN)
作者
Milan Parmar; Di Wang; Ah-Hwee Tan; Chunyan Miao; Jianhua Jiang; You Zhou;
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作者单位

College of Computer Science and Technology, Jilin University, Changchun, China;

Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly;

Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly;

Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly;

School of Management Science and Information Engineering Jilin University of Finance and Economics Changchun, China;

College of Computer Science and Technology, Jilin University, Changchun, China;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Clustering algorithms; Clustering methods; Security; Pattern analysis; Cybernetics; Iris; Conferences;

机译：聚类算法;聚类方法;安全性;模式分析; Cybernetics; Iris;会议;;

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专利

1. REDPC: A residual error-based density peak clustering algorithm [J] . Parmar Milan, Wang Di, Zhang Xiaofeng, Neurocomputing . 2019,第JULa5期

机译：REDPC：基于残差的密度峰值聚类算法
2. REDPC: A residual error-based density peak clustering algorithm [J] . Parmar Milan, Wang Di, Zhang Xiaofeng, Neurocomputing . 2019,第Jul5期

机译：Redpc：基于残留的基于误差的密度峰值聚类算法
3. Drug-target interaction data cluster analysis based on improving the density peaks clustering algorithm [J] . Guo Maozu, Yu Donghua, Liu Guojun, Intelligent data analysis . 2019,第6期

机译：药物目标交互数据集群分析基于改进密度峰聚类算法
4. A novel density peak clustering algorithm based on squared residual error [C] . Milan Parmar, Di Wang, Ah-Hwee Tan, International Conference on Security, Pattern Analysis, and Cybernetics . 2017

机译：一种基于平方残差误差的新型密度峰聚类算法
5. On density-based and representative-based spatial clustering algorithms. [D] . Chen, Chun-Sheng. 2011

机译：基于密度和基于代表的空间聚类算法。
6. A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks [O] . Jiande Fan, Weixin Xie, Haocui Du 2020

机译：基于密度峰值的鲁棒多传感器数据融合聚类算法
7. A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection [O] . Huang Lan, Wang Guishen, Wang Yan, 22

机译：基于密度峰自动选择的重叠社区检测链路密度聚类算法

A novel density peak clustering algorithm based on squared residual error

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