A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

The density peaks clustering (DPC) algorithm is not sensitive to the recognition of halo nodes. The halo nodes at the edge of the density peaks clustering algorithm has a lower local density. The outliers are distributed in halo nodes. The novel halo identification method based on density peaks clus...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.174380-174390
Main Authors: Wang, Limin, Zhou, Wei, Wang, Honghuan, Parmar, Milan, Han, Xuming
Format: Article
Language:English
Subjects:
Algorithms
Classification algorithms
Clustering
Clustering algorithms
Data analysis
Density
Density peaks clustering
Economics
halo node
Identification methods
K-nearest neighbor
K-nearest neighbors algorithm
Kernel
low-density nodes
Nodes
Outliers (statistics)
Partitioning algorithms
Prediction algorithms
Shape
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Summary:The density peaks clustering (DPC) algorithm is not sensitive to the recognition of halo nodes. The halo nodes at the edge of the density peaks clustering algorithm has a lower local density. The outliers are distributed in halo nodes. The novel halo identification method based on density peaks clustering algorithm utilize the advantage of DBSCAN algorithm to quickly identify outliers, which improved the sensitivity to halo nodes. However, the identified halo nodes cannot be effectively assigned to adjacent clusters. Therefore, this paper will use K-nearest neighbor (KNN) algorithm to classify the identified halo nodes. K-nearest neighbor is the simplest and most efficient classification method. The KNN algorithm has the advantages of high accuracy, insensitivity to outliers and no input hypothesis data. Hence, we proposed a novel density peaks clustering halo node assignment algorithm based on K-nearest neighbor theory (KNN-HDPC). KNN-HDPC can grasp the internal relations between outliers and cluster nodes more deeply, so as to dig out the deeper relations between nodes. Experimental results demonstrate that the proposed algorithm can effectively cluster and reclassify a large number of complex data. We can quickly dig out the potential relationship between noise points and cluster points. The improved algorithm has higher clustering accuracy than the original DPC algorithm, and essentially has more robust clustering results.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2957242