Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75795
Title: An adaptive dual clustering algorithm based on hierarchical structure : a case study of settlement zoning
Authors: Liu, YL
Wang, XM
Liu, DF
Liu, LL 
Keywords: Adaptive dual clustering
Data mining
Delaunay triangulation
Hierarchical structure
Rural settlement zoning
Issue Date: 2017
Publisher: Wiley-Blackwell
Source: Transactions in GIS, 2017, v. 21, no. 5, p. 916-933 How to cite?
Journal: Transactions in GIS 
Abstract: 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.
URI: http://hdl.handle.net/10397/75795
ISSN: 1361-1682
EISSN: 1467-9671
DOI: 10.1111/tgis.12246
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