Polygon Cluster Pattern Mining Based on Gestalt Principles
Polygon Cluster Pattern Mining Based on Gestalt Principles
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发表时间：
2007
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通讯作者：
Guo Ren-zhong
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作者：
Guo Ren-zhong
空间聚类对象的模式识别是数据挖掘领域的一个活跃问题。将格式塔原理和空间聚类方法相结合，研究了空间认知原理在这一问题中的应用。为了取代传统的欧几里德距离，提出了一种新的距离概念，即视觉距离来表示相邻实体之间的差异，该模型在Delaunay三角剖分的基础上，提出了一种类似Voronoi图的几何结构来计算视觉距离；基于MST结构的聚类方法表明，基于MST结构的建筑群识别与人工识别是一致的。本研究试图说明，在地理信息系统领域利用常用的数学模型来设计改进的模型，必须考虑空间认知的影响，从识别的角度考虑感知、识别、识别和推理等心理过程。
The pattern recognition of spatial cluster object is an active issue in the field of data mining.This study attempts to investigate the application of spatial cognition principles in this question combing the Gestalt principles and spatial clustering method.To replace the traditional Euclidean distance,a new distance concept,namely visual distance is built to represent the difference between neighbor entities,which considers the difference not only in geometric position but also in size and layout orientation.Because these factors greatly affect the visual judgment in spatial cognition.Based on the Delaunay triangulation the study presents the geometric construction similar to Voronoi diagram to compute the visual distance.The clustering method based on MST structure shows the building group recognition is consistent with the manual identification.This study tries to state that the utilization of common mathematic model in GIS domain has to take into account the impacts of spatial cognition to design the improved model which from the perspective of recognition considers the psychological process,such as perception,identification,recognition and reasoning.