Density Peak Clustering Based on Relative Density Optimization

Li, Chunzhong; Zhang, Yunong

doi:10.1155/2020/2816102

Cited by 4 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simultaneously, MNNC does not require users to decide the parameters, whereas both DBSCAN and k-mean require users to define two parameters. To clearly describe the clustering results, we used ACC and ARI to evaluate the clustering results [30]. The results are shown in Table 1.…”

Section: Definition 4 Distance Attribute Of the Sample (D I )mentioning

confidence: 99%

RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control

Zheng

Jung

Kim

2021

Sensors

View full text Add to dashboard Cite

Radial basis function neural networks are a widely used type of artificial neural network. The number and centers of basis functions directly affect the accuracy and speed of radial basis function neural networks. Many studies use supervised learning algorithms to obtain these parameters, but this leads to more parameters that need to be determined, thereby making the system more complex. This study proposes a modified nearest neighbor-based clustering algorithm for training radial basis function neural networks. The calculation of this clustering algorithm is not large, and it can adapt to varying densities. Furthermore, it does not require researchers to set parameters based on experience. Simulation proves that the clustering algorithm can effectively cluster samples and optimize the abnormal samples. The radial basis function neural network based on modified nearest neighbor-based clustering has higher accuracy in curve fitting than the conventional radial basis function neural network. Finally, the path tracking control based on a radial basis function neural network of a magnetic microrobot is investigated, and its effectiveness is verified through simulation. The test accuracy and training accuracy of the radial basis function neural network was improved by 23.5% and 7.5%, respectively.

show abstract

Section: Definition 4 Distance Attribute Of the Sample (D I )mentioning

confidence: 99%

RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control

Zheng

Jung

Kim

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The drawback of such method was processing certain datasets changing the densities and the irregular shape. Li (2020) [10] have found the DPC based in the relative optimization was used to CFS and detect density peaks presents a completely novel clustering frame and re-defined clustering centre type. With DPC, model data density peaks were simply and rapidly seen.…”

Section: Related Workmentioning

confidence: 99%

An Energy Effectual Swarm Based Hierarchical Density Peak Clustering (HDPCS) Method for Multi-sensor Data Fusion Strategy

Brumancia¹,

Samuel²

2021

IJIES

View full text Add to dashboard Cite

Clustering is the most frequently used physical architecture in data fusion, causing a problem as it has random distribution within local clusters, we propose a density peak based clustering algorithm utilizing a hierarchical approach. As a proficient method to joint decision-making of multi sensors, data fusion contains several benefits in data mining. In this work, we use the Salp Swarm Optimizer (SSO) algorithm based Hierarchical Density Peak Clustering (DPC) method to cluster the multiple sensor nodes as per the data similitude for the process of data fusion. Here, the higher density, distance and the cut off values are determined by using the SSO algorithm and then the chosen optimal fixed values were used to further process. Hierarchical Density Peak Clustering estimates the density and distance of each point. It is validated experimentally and compared it with the datasets and the error prediction with the test case numbers. The experimental results of the method are 96% accuracy determined from different datasets.

show abstract

“…They then determined the clustering centers adaptively by using the density of grid cells instead of the local density of DPC. Li et al [13] defined local relative densities to identify clustering centers of non-uniformly distributed datasets by considering information about the nearest neighbors of sample point truncation distance d c . Hou et al [14] introduced the concept of sample point affiliation to describe the relative density relationship and used the number of affiliated sample points as a criterion to determine the clustering centers.…”

Section: Introductionmentioning

confidence: 99%

SM-DPC: Clustering by Fast Search and Find of Density Peaks Based on SNN With Multi-Cluster Fusion Strategy

Zhou,

Peng,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The Clustering by Fast Search and Find of Density Peaks (DPC) algorithm is a clustering method that automatically identifies clustering centers based on density and relative distance. It has several advantages, including the ability to identify arbitrarily shaped clusters and requiring few input parameters. However, the density measure used in DPC does not consider the spatial distribution characteristics of the sample points in the data set. The clustering performance is suboptimal for datasets with significant differences in cluster density. Additionally, its non-central sample point assignment method is less error-tolerant, which can result in successive assignment errors and a domino effect, ultimately leading to poor clustering accuracy. To address these shortcomings, we propose an improved DPC algorithm based on shared nearest neighbor and multi-cluster fusion (SM-DPC). The local density of sample points is redefined using K-nearest neighbors, which makes the density metric more consistent with the local structural characteristics of the dataset. A two-step allocation strategy for non-central sample points based on shared nearest neighbors is proposed to improve the accuracy of allocation of non-central sample points. A multi-cluster fusion strategy is used to correct the centroid selection bias for datasets where sample points are not uniformly distributed. The experimental results demonstrate that SM-DPC is capable of clustering datasets with arbitrary shape and density distributions effectively. Furthermore, it exhibits superior performance and broader adaptability to different types of datasets compared to DBSCAN, K-means algorithms, and other DPC optimization algorithms.INDEX TERMS K-nearest neighbor, local density, multi-cluster fusion, density peaks

show abstract

Density Peak Clustering Based on Relative Density Optimization

Cited by 4 publications

References 31 publications

RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control

RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control

An Energy Effectual Swarm Based Hierarchical Density Peak Clustering (HDPCS) Method for Multi-sensor Data Fusion Strategy

SM-DPC: Clustering by Fast Search and Find of Density Peaks Based on SNN With Multi-Cluster Fusion Strategy

Contact Info

Product

Resources

About