A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

Liu Yaohui; Liu Dong; Yu Fang; Ma Zhengming

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A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

机译：基于反向邻居的新型密度分层聚类算法

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Clustering is widely used in data analysis, and density-based methods are developed rapidly in the recent 10years. Although the state-of-art density peak clustering algorithms are efficient and can detect arbitrary shape clusters, they are nonsphere type of centroid-based methods essentially. In this paper, a novel local density hierarchical clustering algorithm based on reverse nearest neighbors, RNN-LDH, is proposed. By constructing and using a reverse nearest neighbor graph, the extended core regions are found out as initial clusters. Then, a new local density metric is defined to calculate the density of each object; meanwhile, the density hierarchical relationships among the objects are built according to their densities and neighbor relations. Finally, each unclustered object is classified to one of the initial clusters or noise. Results of experiments on synthetic and real data sets show that RNN-LDH outperforms the current clustering methods based on density peak or reverse nearest neighbors.

机译：聚类广泛用于数据分析中，并且在最近的10年内迅速发展了基于密度的方法。尽管最先进的密度峰聚类算法是有效的并且可以检测任意形状簇，但它们是基本上基于质心的基于质心性的。本文提出了一种基于反向最近邻居，RNN-LDH的新型密度分层聚类算法。通过构造和使用反向最近邻图，将扩展的核心区域被发现为初始集群。然后，定义了新的局部密度度量以计算每个对象的密度;同时，物体之间的密度分层关系是根据其密度和邻居的关系构建的。最后，每个未刻板的对象被分类为初始簇或噪声之一。合成和实数据集的实验结果表明，RNN-LDH基于密度峰值峰值峰值或反向最近邻居的当前聚类方法表现出。

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《Mathematical Problems in Engineering》 |2019年第17期|2959017.1-2959017.10|共10页
作者
Liu Yaohui; Liu Dong; Yu Fang; Ma Zhengming;
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Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Guangdong Peoples R China|Xiangnan Univ Sch Software & Commun Engn Chenzhou 423000 Hunan Peoples R China;

Xiangnan Univ Sch Software & Commun Engn Chenzhou 423000 Hunan Peoples R China;

Xiangnan Univ Sch Software & Commun Engn Chenzhou 423000 Hunan Peoples R China;

Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Guangdong Peoples R China;

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1. A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors [J] . Yaohui Liu, Dong Liu, Fang Yu, Mathematical Problems in Engineering: Theory, Methods and Applications . 2019,第1期

机译：基于反向邻居的新型密度分层聚类算法
2. Facioscapulohumeral Muscular Dystrophy Diagnosis Using Hierarchical Clustering Algorithm and K-Nearest Neighbor Based Methodology [J] . Divya An International journal of e-health and medical communications. . 2017,第2期

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3. A novel clustering algorithm based on the natural reverse nearest neighbor structure [J] . Dai Qi-Zhu, Xiong Zhong-Yang, Xie Jiang, Information Systems . 2019,第SEPa期

机译：基于自然反向最近邻结构的新型聚类算法
4. Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors [C] . Chunhua Ren, Linfu Sun, Qishi Wu International Conference on Intelligent Systems and Knowledge Engineering . 2019

机译：基于分层K最近邻的密度峰聚类研究
5. Voting Nearest Neighbors: SVM Constraints Selection Algorithm Based on K-Nearest Neighbors [D] . Moreira da Costa, Leandro. 2019

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6. A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality [O] . Xueyi Wang -1

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7. Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment [O] . Donghua Yu, Guojun Liu, Maozu Guo, 2019

机译：基于加权局部密度序列和最近邻分配的密度峰集聚类

A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

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