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

ISPRS International Journal of Geo-Information

Chudong Huang¹  Qingyun Du²  Zhi Dong²  Fu Ren² 

[1] College of Civil Engineering and Architecture, Zhejiang University of Technology, 18 Chaowang Road, Hangzhou 310014, China;School of Resources and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China;

关键词: ontologies;    geospatial clustering;    geospatial data mining;    semantic constraints;    DBSCAN;   

DOI  :  10.3390/ijgi5050072

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown