基于DBSCAN的动态邻域密度聚类算法

doi:10.11896/jsjkx.220400127

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220400127-7.doi: 10.11896/jsjkx.220400127

• Big Data & Data Science • Previous Articles Next Articles

Dynamic Neighborhood Density Clustering Algorithm Based on DBSCAN

ZHANG Peng, LI Xiaolin, WANG Liyan

College of mines,China University of Mining and Technology,Xuzhou,Jiangsu 221003,China

Online:2023-06-10 Published:2023-06-12
About author:ZHANG Peng,born in 1991,postgra-duate.His main research interests include data analysis and processing. LI Xiaolin,born in 1986,Ph.D,professor.His main research interests include enterprise management informatization and system integration.
Supported by:
National Natural Science Foundation of China(71401164).

Abstract

Abstract: The traditional density clustering algorithms do not consider the attribute difference between data points in the clustering process,but treat all data points as homogenous points.Based on the traditional DBSCAN algorithm,a dynamic neighborhood－－density based spatial clustering of applications with noise(DN-DBSCAN) is proposed.When it is working,each point’s neighborhood radius is determined by the properties of itself,so the neighborhood radius is dynamic changing.Thus,different influences on datasets produced by points with different properties is reflected in the clustering results,making the density clustering algorithm has more practical meaning and can be more reasonable to solve practical problems.On the basis of example analysis,the DN-DBSCAN algorithm is applied to solve the urban agglomeration division problem in the Yangtze river delta,and the results of DBSCAN algorithm,OPTICS algorithm and DPC algorithm are compared and analyzed.The results show that DN-DBSCAN algorithm can reasonably classify urban agglomerations in the Yangtze river delta according to the different attributes of each city with an accuracy of 95%,which is much higher than the accuracy of 85%,85% and 88% of the other three algorithms respectively,indicating that it has a better ability to solve practical problems.

Key words: Dynamic neighborhood, Density clustering, Dynamic neighborhood density clustering, Attribute differences, Division accuracy

CLC Number:

TP301

ZHANG Peng, LI Xiaolin, WANG Liyan. Dynamic Neighborhood Density Clustering Algorithm Based on DBSCAN[J].Computer Science, 2023, 50(6A): 220400127-7.

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