含关键特征的显著Co-location模式挖掘研究

空间Co-location模式是一组在空间中频繁并置的空间特征的子集。空间Co-location模式挖掘通常假设空间实例之间相互独立，然而，在实际应用中，不同空间特征、不同实例之间往往相互作用或依赖。空间Co-location关键特征是指对模式具有主导作用的特征。在频繁模式中，识别含关键特征的Co-location模式并摘取模式中的关键特征，为用户提供更精简的挖掘结果，提高Co-location模式的可用性，对Co-location模式挖掘具有重要意义。本文首先定义了含有关键特征的显著频繁Co-location模式新概念，以及一系列度量指标以识别显著频繁Co-location模式中的关键特征；其次，给出了一个挖掘显著频繁Co-location模式和关键特征的算法；最后，在模拟和真实数据集上进行了大量的实验，验证了所提出算法的效果及性能。

Mining Spatial Co-location Patterns with Key Features

The co-location pattern mining discovers the subsets of spatial features which are located together frequently in geography. Instance independence has been taken as a major assumption in the co-location mining based on prevalence framework. However, in real-world spatial data sources, spatial instances are more or less correlated with each other. Prevalence-based framework can do limited work in spatial instance correlated analysis. For reducing the co-location mining results and promoting the usability of co-location patterns, this paper proposed a new framework to identify the co-location patterns with key features and extract the key features from a large collection of prevalent co-location patterns. We first give the definitions of significant co-location patterns; secondly, we design a series of metrics to evaluate significance of co-location patterns and extract the key features; thirdly, an efficient algorithm is proposed to mine the significant co-location pattern with key features. The experiments evaluate the method both on real data sets and synthetic data sets. The results show that our method can effectively identify the significant co-location patterns with key features.