A physical model inspired density peak clustering.
A physical model inspired density peak clustering.
复制标题
受物理模型启发的密度峰值聚类
DOI：
10.1371/journal.pone.0239406
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Wang H
中科院分区：
文献类型：
--
作者：
Zhuang H;Cui J;Liu T;Wang H
聚类是数据挖掘的一项重要技术，在生物科学、社会网络和网络分析中发挥着重要作用。密度峰值聚类作为一种基于密度和距离的聚类算法，被广泛应用于解决实际问题。该算法假设聚类中心具有较大的局部密度，并且距离密度较高的点较远。然而，密度峰值聚类算法对密度和距离高度敏感，并且不能准确地识别在聚类结构中具有显著差异的数据集中的聚类。另外，密度峰值聚类算法的分配策略在数据点分配中容易造成附加分配错误。为了解决这些问题，本研究提出了一种基于势场扩散的密度峰聚类方法。与现有的聚类算法相比，基于势场扩散的密度峰值聚类算法具有以下三个方面的优点：1）引入势场概念，提出了一种基于势场扩散的密度测度。利用该测度可以准确地选择聚类中心。2)基于势场扩散的密度峰值聚类算法定义了相似点的判断条件，对不同点采用不同的分配策略，避免了数据点分配中的附加误差。3)该研究在合成和真实世界的数据集上进行了许多实验。实验结果表明，基于势场扩散的密度峰值聚类算法具有良好的聚类效果，适用于不同大小、维度和形状的复杂数据集。此外，所提出的基于势场扩散的密度峰值聚类算法在变密度和非凸数据集上表现出特别优异的性能。
Clustering is an important technology of data mining, which plays a vital role in bioscience, social network and network analysis. As a clustering algorithm based on density and distance, density peak clustering is extensively used to solve practical problems. The algorithm assumes that the clustering center has a larger local density and is farther away from the higher density points. However, the density peak clustering algorithm is highly sensitive to density and distance and cannot accurately identify clusters in a dataset having significant differences in cluster structure. In addition, the density peak clustering algorithm’s allocation strategy can easily cause attached allocation errors in data point allocation. To solve these problems, this study proposes a potential-field-diffusion-based density peak clustering. As compared to existing clustering algorithms, the advantages of the potential-field-diffusion-based density peak clustering algorithm is three-fold: 1) The potential field concept is introduced in the proposed algorithm, and a density measure based on the potential field’s diffusion is proposed. The cluster center can be accurately selected using this measure. 2) The potential-field-diffusion-based density peak clustering algorithm defines the judgment conditions of similar points and adopts different allocation strategies for dissimilar points to avoid attached errors in data point allocation. 3) This study conducted many experiments on synthetic and real-world datasets. Results demonstrate that the proposed potential-field-diffusion-based density peak clustering algorithm achieves excellent clustering effect and is suitable for complex datasets of different sizes, dimensions, and shapes. Besides, the proposed potential-field-diffusion-based density peak clustering algorithm shows particularly excellent performance on variable density and nonconvex datasets.
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