An Ontology Assisted Framework Co-location Pattern Mining

The importance of spatial data mining is growing with the increasing incidence and importance of large geo-spatial datasets such as maps, location based mobile app data, medical data, crime data, education system data, traffic data and many more. Co-location pattern mining is one of the important task in spatial data mining. The co-location patterns represent subsets of Boolean spatial features whose instances are often located in close geographic proximity. It is generally seen as a descriptive task where values of attributes are taken as deciding factors and the meaning of each item or instance is not taken into consideration. In this work we propose an Ontology assisted co-location mining framework for co-location pattern discovery. Ontologies of spatial attributes and spatial co-location pattern discovery process interoperate to deliver a concise framework for co-location pattern mining. The framework is tested on the Indian schools dataset provided by District Information System for Education (DISE).