星型高影响的空间co-location模式挖掘

Mining Spatial co -location Patterns with Star High Influence

空间co-location模式是其实例在空间邻域内频繁并置出现的一组空间特征集。传统的空间co-location模式挖掘方法通常假设空间实例相互独立,并采用参与度作为模式有趣性的唯一度量指标,没有考虑不同特征或相同特征不同实例在空间邻域内所产生的影响差异,因此挖掘的结果往往缺乏相关性和可解释性。文中提出了一种星型高影响的空间co-location模式及挖掘方法,能够有效发现自身影响高且在邻域范围内也具有一定影响的空间co-location模式。首先,定义了度量模式影响的两个指标:模式影响参与度和模式影响占有度。其次,提出了挖掘星型高影响co-location模式的基础挖掘算法和剪枝策略。最后,通过在大量的真实和合成数据集上进行实验,分析了挖掘算法的效率和挖掘效果。实验结果表明,所提出的星型高影响co-location模式的度量方法和挖掘算法能够挖掘出较强相关性的co-location模式。

The spatial co-location pattern is a group of spatial features whose instances are frequently collocated in the spatial neighborhood.Traditional spatial co-location pattern mining methods usually assume that the spatial instances are independent each other,and use participation index(PI)to measure the patterns.They don’t consider the influence of different features or different instances of the same feature so that the mining results are often lack of relevance and interpretability.This paper proposes the spatial co-location pattern with star high influence which has influence in the neighborhood,and its mining method.Firstly,this paper defines two indicators to measure the influence of the pattern:influence participation index(IPI)and influence occupancy index(IOI).Secondly,a basic algorithm and pruning strategies for mining co-location patterns with star high influence are proposed.Finally,the experimental results on real and synthetic data sets show that the proposed method can discover the strong relevant co-location patterns.