scholarly journals An Adaptive Density Peaks Clustering Method With Fisher Linear Discriminant

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 72936-72955 ◽  
Author(s):  
Lin Sun ◽  
Ruonan Liu ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Clustering Method ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Density Peaks ◽  
Density Peaks Clustering

PSO-CFDP: A Particle Swarm Optimization-Based Automatic Density Peaks Clustering Method for Cancer Subtyping

2019 ◽  
Vol 84 (1) ◽  
pp. 9-20
Author(s):  
Xuhui Zhu ◽  
Junliang Shang ◽  
Yan Sun ◽  
Feng Li ◽  
Jin-Xing Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Clustering Method ◽  
Swarm Optimization ◽  
Density Peaks ◽  
Density Peaks Clustering

scholarly journals A Fast Density Peak Clustering Method with Autoselect Cluster Centers

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Zhihe Wang ◽  
Yongbiao Li ◽  
Hui Du ◽  
Xiaofen Wei
Keyword(s):  
Experimental Results ◽  
Cluster Center ◽  
Clustering Method ◽  
Density Peak ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Manual Selection ◽  
Density Peak Clustering ◽  
Selection Of

Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.

scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

Sign in / Sign up

Export Citation Format

Share Document