作者：Liu, YL (Liu, Yaolin); Wang, XM (Wang, Xiaomi); Liu, DF (Liu, Dianfeng); Liu, LL (Liu, Leilei)
来源出版物：TRANSACTIONS IN GIS 卷：21 期：5 页码: 916-933 DOI：10.1111/tgis.12246 出版年：OCT 2017
摘要：Traditional dual clustering algorithms cannot adaptively perform clustering well without sufficient prior knowledge of the dataset. This article aims at accommodating both spatial and non-spatial attributes in detecting clusters without the need to set parameters by default or prior knowledge. A novel adaptive dual clustering algorithm (ADC+) is proposed to obtain satisfactory clustering results considering the spatial proximity and attribute similarity with the presence of noise and barriers. In this algorithm, Delaunay triangulation is utilized to adaptively obtain spatial proximity and spatial homogenous patterns based on particle swarm optimization (PSO). Then, a hierarchical clustering method is employed to obtain clusters with similar attributes. The hierarchical clustering method adopts a discriminating coefficient to adaptively control the depth of the hierarchical architecture. The clustering results are further refined using an optimization approach. The advantages and practicability of the ADC+ algorithm are illustrated by experiments on both simulated datasets and real-world applications. It is found that the proposed ADC+ algorithm can adaptively and accurately detect clusters with arbitrary shapes, similar attributes and densities under the consideration of barriers.
作者关键词： adaptive dual clustering; data mining; Delaunay triangulation; hierarchical structure; rural settlement zoning
通讯作者地址：Wang, XM (reprint author), Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan, Hubei, Peoples R China.
[Liu, Yaolin; Wang, Xiaomi; Liu, Dianfeng] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan, Hubei, Peoples R China.
[Liu, Yaolin] Wuhan Univ, Minist Educ, Key Lab Geog Informat Syst, 129 Luoyu Rd, Wuhan, Hubei, Peoples R China.
[Liu, Yaolin] Wuhan Univ, Collaborat Innovat Ctr Geospatial Informat Techno, 129 Luoyu Rd, Wuhan, Hubei, Peoples R China.
[Liu, Leilei] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Hong Kong, Peoples R China.
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