Discovering Interesting Co-location Patterns Interactively Using Ontologies

Co-location pattern mining, which discovers feature types that frequently appear in a nearby geographic region, plays an important role in spatial data mining. Common frameworks for mining co-location patterns generate numerous redundant patterns. Thus, several methods were proposed to overcome this drawback. However, most of these methods did not guarantee that the extracted co-location patterns were interesting for being 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 discover interesting 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 interesting co-location patterns efficiently. Finally, a filter was designed to reduce the number of discovered co-location patterns in the result set further. The experimental results on both synthetic and real data sets demonstrated the effectiveness of our approach.