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硕士生王天富的论文在ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 刊出
发布时间:2019-07-09 08:15:04     发布者:易真     浏览次数:

标题: NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space

作者: Wang, TF (Wang, Tianfu); Ren, C (Ren, Chang); Luo, Y (Luo, Yun); Tian, J (Tian, Jing)

来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION  : 8  : 5  文献号: 218  DOI: 10.3390/ijgi8050218  出版年: MAY 2019  

摘要: Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched. This study aimed to present a well-known clustering algorithm, named density-based spatial clustering of applications with noise (DBSCAN), to network space and proposed a new clustering algorithm named network space DBSCAN (NS-DBSCAN). Basically, the NS-DBSCAN algorithm used a strategy similar to the DBSCAN algorithm. Furthermore, it provided a new technique for visualizing the density distribution and indicating the intrinsic clustering structure. Tested by the points of interest (POI) in Hanyang district, Wuhan, China, the NS-DBSCAN algorithm was able to accurately detect the high-density regions. The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT) in terms of their effectiveness. The hierarchical clustering algorithm was effective only when the cluster number was well specified, otherwise it might separate a natural cluster into several parts. The NC_DT method excessively gathered most objects into a huge cluster. Quantitative evaluation using four indicators, including the silhouette, the R-squared index, the Davis-Bouldin index, and the clustering scheme quality index, indicated that the NS-DBSCAN algorithm was superior to the hierarchical clustering and NC_DT algorithms.

入藏号: WOS:000470965400019

语言: English

文献类型: Article

作者关键词: clustering analysis; DBSCAN algorithm; network spatial analysis; spatial data mining

KeyWords Plus: LOCAL INDICATORS; CONSTRAINED CLUSTERS; PATTERNS; REGIONALIZATION; ASSOCIATION

地址: [Wang, Tianfu; Luo, Yun; Tian, Jing] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Ren, Chang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China.

[Tian, Jing] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Tian, Jing] Natl Adm Surveying Mapping & Geoinformat, Key Lab Digital Mapping & Land Informat Applicat, Wuhan 430079, Hubei, Peoples R China.

通讯作者地址: Tian, J (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

Tian, J (通讯作者)Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

Tian, J (通讯作者)Natl Adm Surveying Mapping & Geoinformat, Key Lab Digital Mapping & Land Informat Applicat, Wuhan 430079, Hubei, Peoples R China.

电子邮件地址: tianfu.wang@whu.edu.cn; imrc@whu.edu.cn; Yun_Luo@whu.edu.cn; tianjing_sres@whu.edu.cn

影响因子:1.84


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