Spatial co-location mining is a useful tool for discovering spatial association patterns of feature sets which are frequently observed together in nearby geographic space. Most of co-location mining techniques aim to find all prevalent co-located feature sets which satisfy a given prevalence threshold. However the result is often large, especially when the prevalence threshold is set low, or long co-location patterns present. Moreover the output has many redundant information which makes it difficult for users to filter useful patterns. This work introduces the problem of mining reduced sets of co-location patterns in order to concisely represent interesting spatial relationship patterns. With aiming two such outputs in the form of maximal and closed co-locations, this paper proposes an algorithmic framework to discover maximal co-location patterns and closed co-location patterns as well as all prevalent co-location patterns, and presents the algorithm details for each pattern discovery. The developed algorithms are correct and complete in finding maximal co-locations and closed co-locations. The experiment result shows that the framework reduces candidate feature sets effectively and finds co-location patterns efficiently.
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