基于数据场的改进DBSCAN 聚类算法.pdf

文档介绍：ISSN 1673-9418 CODEN JKYTA8
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Journal of Frontiers puter Science and Technology

1673-9418/2012/06(10)-0903-09
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DOI: .1673-
基于数据场的改进DBSCAN聚类算法*
杨静 1,2+,高嘉伟 1,2,梁吉业 1,2,刘杨磊 1,2
1. 山西大学计算智能与中文信息处理教育部重点实验室,太原 030006
2. 山西大学计算机与信息技术学院,太原 030006
An Improved DBSCAN Clustering Algorithm Based on Data Field􀆽
YANG Jing1,2+, GAO Jiawei1,2, LIANG Jiye1,2, LIU Yanglei1,2
1. Key Laboratory putational Intelligence and Chinese Information Processing of Ministry of Education,
Shanxi University, Taiyuan 030006, China
2. School puter and Information Technology, Shanxi University, Taiyuan 030006, China
+ Corresponding author: E-mail: morgan1127@
YANG Jing, GAO Jiawei, LIANG Jiye, et al. An improved DBSCAN clustering algorithm based on data field.
Journal of Frontiers puter Science and Technology, 2012, 6(10):903-911.
Abstract: DBSCAN (density based spatial clustering of applications with noise) algorithm is a typical density-based
clustering algorithm. The algorithm can discover the arbitrary-shaped clusters. However, the clustering results depend
on the two parameters Eps and MinPts which are chosen by users. And for some datasets with large density differences,
either the clustering results may have the incorrect cluster number, or the algorithm may label part of the data as
noise. Using the advantages