空间co-location模式的主导特征挖掘

Mining Spatial co-location Pattern with Dominant Feature

空间co-location模式是空间特征的子集,它们的实例在邻域内频繁并置出现。传统co-location模式不区分模式中特征的重要性,忽略了特征间的主导关系。主导特征co-location模式考虑模式中特征的不平等性,分析特征间的主导关系,具有重要的应用意义。然而,现有主导特征模式挖掘没有从特征实例分布的角度综合考虑一个特征主导其他特征的可能倾向和影响强度,使得挖掘的主导特征及模式没有较好地反映特征间的主导关系。首先分析co-location模式中特征实例的空间分布,提出模式主导度,用以度量模式中某个特征主导其他特征的可能倾向;提出主导影响度,用以度量模式中某个特征主导其他特征的影响强度;基于这两个新度量,提出co-location模式的主导特征挖掘。然后通过优化新度量的计算,提出有效的主导特征colocation模式挖掘算法。在真实数据集和合成数据集上开展大量实验,验证了所提方法能够有效地识别co-location模式中的主导特征,所提算法能够高效地挖掘主导特征及模式。

A spatial co-location pattern is a subset of spatial features whose instances frequently locate together in the neighborhood.Traditional co-location pattern does not distinguish the importance of features in the pattern,and ignores the dominant relationship among features.The co-location pattern with dominant feature considers the inequality of features in the pattern,and analyzes the dominant relationship among features,which can be used in many applications.However,the existing methods for mining co-location pattern with dominant feature do not comprehensively consider the possible tendency and influence intensity of one feature dominating other features from the perspective of features’instances distribution,so that the dominant relationship among features is not properly revealed.This paper first analyzes the spatial distribution of features’instances in a co-location pattern,proposes the pattern dominance index to measure the possible tendency of a feature dominating other features in a pattern,and proposes the dominant influence index to measure the influence intensity of the dominance tendency.Based on the two new measures,the dominant feature mining of co-location pattern is proposed.Then an efficient algorithm for mining co-location pattern with dominant feature is proposed by optimizing the calculation of new measures.A large number of experiments on real data sets and synthetic data sets verify that the proposed method can effectively identify the dominant feature in a co-location pattern,and it can efficiently mine co-location patterns with dominant feature.