Parameter selection algorithm of DBSCAN based on K-means two classification algorithm

Parameter selection algorithm of DBSCAN based on K-means two classification algorithm

Clustering algorithm is one of the most important algorithms in unsupervised learning. For density-based spatial clustering of applications with noise (DBSCAN) density clustering algorithm, the selection of neighborhood radius and minimum number is the key to get the best clustering results. Aiming...

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Bibliographic Details
Main Authors: Shouhong Chen, Xinyu Liu, Jun Ma, Shuang Zhao, Xingna Hou
Format: Article
Language:English
Published: Wiley 2019-10-01
Series:The Journal of Engineering
Subjects:
data analysis
unsupervised learning
pattern clustering
pattern classification
human factors
start critical value clustering
dbscan algorithm
neighbourhood radius
experimental data analysis show
minimum point number
maximum distance value
cluster centre-to-centre distance
data points
clustering centres
noise density clustering algorithm
classification algorithm
parameter selection algorithm
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9082

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