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ISPRS Int. J. Geo-Inf. 2016, 5(5), 72; doi:10.3390/ijgi5050072

Density-Based Clustering with Geographical Background Constraints Using a Semantic Expression Model

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School of Resources and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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Key Laboratory of Digital Mapping and Land information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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The First Surveying and Mapping Institute of Zhejiang Province, 135 Jiaogong Road, Hangzhou 31012, China
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College of Civil Engineering and Architecture, Zhejiang University of Technology, 18 Chaowang Road, Hangzhou 310014, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 7 March 2016 / Revised: 4 May 2016 / Accepted: 9 May 2016 / Published: 19 May 2016

Abstract

A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with Noise) algorithm in the form of knowledge representation for constraint clustering. When used in the process of clustering geographic information, semantic reasoning based on a defined ontology and its relationships is primarily intended to overcome the lack of knowledge of the relevant geospatial data. Better constraints on the geographical knowledge yield more reasonable clustering results. This article uses an ontology to describe the four types of semantic constraints for geographical backgrounds: “No Constraints”, “Constraints”, “Cannot-Link Constraints”, and “Must-Link Constraints”. This paper also reports the implementation of a prototype clustering program. Based on the proposed approach, DBSCAN can be applied with both obstacle and non-obstacle constraints as a semi-supervised clustering algorithm and the clustering results are displayed on a digital map. View Full-Text
Keywords: ontologies; geospatial clustering; geospatial data mining; semantic constraints; DBSCAN ontologies; geospatial clustering; geospatial data mining; semantic constraints; DBSCAN
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Du, Q.; Dong, Z.; Huang, C.; Ren, F. Density-Based Clustering with Geographical Background Constraints Using a Semantic Expression Model. ISPRS Int. J. Geo-Inf. 2016, 5, 72.

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