Ontology-Based Interactive Post-mining of Interesting Co-location Patterns

Abstract

Spatial co-location patterns represent the subsets of spatial features whose instances are frequently located together in geographic space. Common frameworks for mining co-location patterns generate numerous redundant co-location patterns. Thus, several methods were proposed to overcome this drawback. However, most of these methods do not guarantee that the extracted co-location patterns are interesting for the user because they are generally based on statistical information. Thus, it is crucial to help the decision-maker choose interesting co-location patterns with an efficient interactive procedure. This paper proposed an interactive approach to prune and filter discovered co-location patterns. First, ontologies were used to improve the integration of user knowledge. Second, an interactive process was designed to collaborate with the user to find the interesting co-location patterns efficiently. The experimental results on a real data set demonstrated the effectiveness of our approach.