Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship

Spatial co-location pattern mining discovers the subsets of spatial features frequently observed together in nearby geographic space. To reduce time and space consumption in checking the clique relationship of row instances of the traditional co-location pattern mining methods, the existing work ado...

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Autores principales:	Meijiao Wang, Yu chen, Yunyun Wu, Libo He
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	spatial data mining spatial co-location pattern density peak clustering fuzzy neighbor relationship cluster fuzzy participation index Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/d4312ad2d2974781a7262e97cfc2465d
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id	oai:doaj.org-article:d4312ad2d2974781a7262e97cfc2465d
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spelling	oai:doaj.org-article:d4312ad2d2974781a7262e97cfc2465d2021-11-24T01:10:01ZSpatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship10.3934/mbe.20214081551-0018https://doaj.org/article/d4312ad2d2974781a7262e97cfc2465d2021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021408?viewType=HTMLhttps://doaj.org/toc/1551-0018Spatial co-location pattern mining discovers the subsets of spatial features frequently observed together in nearby geographic space. To reduce time and space consumption in checking the clique relationship of row instances of the traditional co-location pattern mining methods, the existing work adopted density peak clustering to materialize the neighbor relationship between instances instead of judging the neighbor relationship by a specific distance threshold. This approach had two drawbacks: first, there was no consideration in the fuzziness of the distance between the center and other instances when calculating the local density; second, forcing an instance to be divided into each cluster resulted in a lack of accuracy in fuzzy participation index calculations. To solve the above problems, three improvement strategies are proposed for the density peak clustering in the co-location pattern mining in this paper. Then a new prevalence measurement of co-location pattern is put forward. Next, we design the spatial co-location pattern mining algorithm based on the improved density peak clustering and the fuzzy neighbor relationship. Many experiments are executed on the synthetic and real datasets. The experimental results show that, compared to the existing method, the proposed algorithm is more effective, and can significantly save the time and space complexity in the phase of generating prevalent co-location patterns.Meijiao WangYu chenYunyun WuLibo HeAIMS Pressarticlespatial data miningspatial co-location patterndensity peak clusteringfuzzy neighbor relationshipcluster fuzzy participation indexBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8223-8244 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	spatial data mining spatial co-location pattern density peak clustering fuzzy neighbor relationship cluster fuzzy participation index Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	spatial data mining spatial co-location pattern density peak clustering fuzzy neighbor relationship cluster fuzzy participation index Biotechnology TP248.13-248.65 Mathematics QA1-939 Meijiao Wang Yu chen Yunyun Wu Libo He Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship
description	Spatial co-location pattern mining discovers the subsets of spatial features frequently observed together in nearby geographic space. To reduce time and space consumption in checking the clique relationship of row instances of the traditional co-location pattern mining methods, the existing work adopted density peak clustering to materialize the neighbor relationship between instances instead of judging the neighbor relationship by a specific distance threshold. This approach had two drawbacks: first, there was no consideration in the fuzziness of the distance between the center and other instances when calculating the local density; second, forcing an instance to be divided into each cluster resulted in a lack of accuracy in fuzzy participation index calculations. To solve the above problems, three improvement strategies are proposed for the density peak clustering in the co-location pattern mining in this paper. Then a new prevalence measurement of co-location pattern is put forward. Next, we design the spatial co-location pattern mining algorithm based on the improved density peak clustering and the fuzzy neighbor relationship. Many experiments are executed on the synthetic and real datasets. The experimental results show that, compared to the existing method, the proposed algorithm is more effective, and can significantly save the time and space complexity in the phase of generating prevalent co-location patterns.
format	article
author	Meijiao Wang Yu chen Yunyun Wu Libo He
author_facet	Meijiao Wang Yu chen Yunyun Wu Libo He
author_sort	Meijiao Wang
title	Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship
title_short	Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship
title_full	Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship
title_fullStr	Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship
title_full_unstemmed	Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship
title_sort	spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/d4312ad2d2974781a7262e97cfc2465d
work_keys_str_mv	AT meijiaowang spatialcolocationpatternminingbasedontheimproveddensitypeakclusteringandthefuzzyneighborrelationship AT yuchen spatialcolocationpatternminingbasedontheimproveddensitypeakclusteringandthefuzzyneighborrelationship AT yunyunwu spatialcolocationpatternminingbasedontheimproveddensitypeakclusteringandthefuzzyneighborrelationship AT libohe spatialcolocationpatternminingbasedontheimproveddensitypeakclusteringandthefuzzyneighborrelationship
_version_	1718416061823451136

Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship

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