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A dual spatial clustering method in the presence of heterogeneity and noise
Author(s) -
Zhu Jie,
Zheng Jiazhu,
Di Shaoning,
Wang Shu,
Yang Jing
Publication year - 2020
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12687
Subject(s) - cluster analysis , delaunay triangulation , computer science , spatial analysis , noise (video) , data mining , similarity (geometry) , pattern recognition (psychology) , dbscan , artificial intelligence , entropy (arrow of time) , correlation clustering , mathematics , algorithm , geography , cure data clustering algorithm , remote sensing , image (mathematics) , physics , quantum mechanics
Abstract The detection of spatial clusters, taking into account both spatial proximity and attribute similarity, plays a vital role in spatial data analysis. Although several dual clustering methods are currently available in the literature, most of them have detected homogeneous spatially adjacent clusters suffering from between‐cluster inhomogeneity and noise, where those spatial points have been described in the attribute domain. This article aims to accommodate both spatial proximity and attribute similarity with the presence of heterogeneity and noise. In this algorithm, Delaunay triangulation with edge‐length constraints, with consideration of arbitrary geometrical shapes, different densities, and spatial noise, is first utilized to construct spatial proximity relationships among points. Then, a clustering strategy employing information entropy is designed to identify clusters having similar attributes. The attribute clustering can adaptively and accurately detect clusters under the consideration of heterogeneity and noise. The efficacy and practicability of the proposed algorithm are illustrated by experiments employing both simulated datasets and real spatial point events.