HaloDPC: An Improved Recognition Method on Halo Node for Density Peak Clustering Algorithm

Jiang, Jianhua; Wei, Zhou; Wang, Limin; Tao, Xin; Li, Keqin

doi:10.1142/s0218001419500125

Cited by 11 publications

(12 citation statements)

References 25 publications

(32 reference statements)

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“…Cluster analysis aims to classify elements into categories on the basis of their similarity [26]. In recent years, many clustering algorithms have been proposed [26][27][28][29]. e density peak clustering has been published by Rodriguez and Laio in Science [26].…”

Section: Related Workmentioning

confidence: 99%

“…Jiang et al [28] developed GDPC algorithm with an alternative decision graph based on gravitation theory and nearby distance to identify centroids and anomalies accurately. In order to overcome the defect of the original DPC in detecting anomalies and hub nodes, Jiang et al [29] proposed an improved recognition method on the halo node for density peak clustering algorithm (halo DPC) [29]. e proposed halo DPC can improve the ability to deal with varying densities, irregular shapes, the number of clusters, outlier, and hub node detection [29].…”

Section: Related Workmentioning

confidence: 99%

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Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

Zhang

Kou

et al. 2020

Complexity

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In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making support for evaluation of clustering algorithms (DMSECA), to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process. The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18,310 instances and 313 attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets. Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

Zhang

Kou

et al. 2020

Complexity

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“…DPC algorithm can identify clusters of arbitrary shape and dimension [32][33][34][35] , but some shortcomings of the algorithm will affect the final clustering results. In addition, the algorithm belongs to the hard clustering algorithm, once a sample is allocated wrongly, the related samples will be misallocated.…”

Section: Related Workmentioning

confidence: 99%

F-DPC: Fuzzy Neighborhood-Based Density Peak Algorithm

Zhou

Wang

2020

IEEE Access

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Clustering is a concept in data mining, which divides a data set into different classes or clusters according to a specific standard, making the similarity of data objects in the same cluster as large as possible. Clustering by fast search and find of density peaks (DPC) is a novel clustering algorithm based on density. It is simple and novel, only requiring fewer parameters to achieve better clustering effect, without the requirement for iterative solution. And it has expandability and can detect the clustering of any shape. However, DPC algorithm still has some defects, such as it employs the clear neighborhood relations to calculate local density, so it cannot identify the neighborhood membership of different values of points from the distance of points and It is impossible to accurately cluster the data of the multi-density peak. The fuzzy neighborhood density peak clustering algorithm is proposed for this shortcoming (F-DPC): novel local density is defined by the fuzzy neighborhood relationship. The fuzzy set theory can be used to make the fuzzy neighborhood function of local density more sensitive, so that the clustering for data set of various shapes and densities is more robust. Experiments show that the algorithm has high accuracy and robustness.

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“…In this paper, we have used some basic ideas and concepts of DPC [4], DBSCAN [21] and HaloDPC [5] algorithms. Therefore, we will briefly introduce the three algorithms in the following sections.…”

Section: Related Workmentioning

confidence: 99%

“…Jiang, proposed the HaloDPC that improved recognition method on halo node for density peaks clustering algorithm [5]. Aiming at the density peaks clustering algorithm, the nodes located in the low-density area of the data set cannot be effectively processed and the outliers cannot be efficiently classified into clusters.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

Self Cite

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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. INDEX TERMS Density peaks clustering, halo node, K-nearest neighbor, low-density nodes.

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HaloDPC: An Improved Recognition Method on Halo Node for Density Peak Clustering Algorithm

Cited by 11 publications

References 25 publications

Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

F-DPC: Fuzzy Neighborhood-Based Density Peak Algorithm

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

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