Discovering Statistically Significant Co-location Rules in Datasets with Extended Spatial Objects

Abstract

Co-location rule mining is one of the tasks of spatial data mining, which focuses on the detection of sets of spatial features that show spatial associations. Most previous methods are generally based on transaction-free apriori-like algorithms which are dependent on user-defined thresholds and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location rule mining problem. To solve these difficulties, a transactionization approach was recently proposed; designed to mine datasets with extended spatial objects. A statistical test is used instead of global thresholds to detect significant co-location rules. One major shortcoming of this work is that it limits the size of antecedent of co-location rules up to three features, therefore, the algorithm is difficult to scale up. In this paper we introduce a new algorithm that fully exploits the property of statistical significance to detect more general co-location rules. We use our algorithm on real datasets with the National Pollutant Release Inventory (NPRI). A classifier is also proposed to help evaluate the discovered co-location rules.