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	A mathematical morphology based scale space method for the mining of linear features in geographic data
	Pei T.
	2006
关键词	mathematical morphology scale space theory clustering spatial data mining linear belt seismic belt clustering-algorithm spatial databases earthquakes
英文摘要	This paper presents a spatial data mining method MCAMMO and its extension L_MCAMMO designed for discovering linear and near linear features in spatial databases. L_MCAMMO can be divided into two basic steps: first, the most suitable re-segmenting scale is found by MCAMMO, which is a scale space method with mathematical morphology operators; second, the segmented result at this scale is re-segmented to obtain the final linear belts. These steps are essentially a multi-scale binary image segmentation process, and can also be treated as hierarchical clustering if we view the points under each connected component as one cluster. The final number of clusters is the one which survives (relatively, not absolutely) the longest scale range, and the clustering which first realizes this number of clusters is the most suitable segmentation. The advantages of MCAMMO in general and L_MCAMMO in particular, are: no need to pre-specify the number of clusters, a small number of simple inputs, capable of extracting clusters with arbitrary shapes, and robust to noise. The effectiveness of the proposed method is substantiated by the real-life experiments in the mining of seismic belts in China.
出处	Data Mining and Knowledge Discovery
卷	12
期	1
页	97-118
收录类别	SCI
语种	英语
ISSN号	1384-5810
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/22894]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Pei T.. A mathematical morphology based scale space method for the mining of linear features in geographic data. 2006.

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