A parallel varied density-based clustering algorithm with optimized data partition-Reference-Cited by-同舟云学术

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A parallel varied density-based clustering algorithm with optimized data partition

Published:2017-07-28 Issue:1 Volume:63 Page:93-114
ISSN:1449-8596
Container-title:Journal of Spatial Science
language:en
Short-container-title:Journal of Spatial Science

Author:

Gu Yuhua¹²^ORCID,Ye Xinyue³,Zhang Feng¹²,Du Zhenhong¹²^ORCID,Liu Renyi²,Yu Lifeng¹

Affiliation:

1. School of the Earth Sciences, Zhejiang University, Hangzhou, China

2. Zhejiang Provincial Key Laboratory of Geographic Information Science, Department of Earth Sciences, Zhejiang University, Hangzhou, China

3. Department of Geography, Kent State University, Kent, OH, USA

Funder

National Natural Science Foundation of China

Major Program of China High Resolution Earth Observation System

Science and Technology Project of Zhejiang Province

Publisher

Informa UK Limited

Subject

Atmospheric Science,General Energy,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/14498596.2017.1352542

Reference50 articles.

1. A matrix-algebraic formulation of distributed-memory maximal cardinality matching algorithms in bipartite graphs

2. Introducing a usability framework to support urban information discovery and analytics

3. Big Data Analytics

4. ST-DBSCAN: An algorithm for clustering spatial–temporal data

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