An order-clique-based approach for mining maximal co-locations
Information Sciences: an International Journal
Mining Spatial Co-location Patterns with Dynamic Neighborhood Constraint
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
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Most algorithms for mining interesting spatial co- locations integrate the co-location / clique generation task with the interesting pattern mining task, and are usually based on the Apriori algorithm. This has two downsides. First, it makes it difficult to meaningfully include certain types of complex relationships especially negative rela- tionships in the patterns. Secondly, the Apriori algorithm is slow. In this paper, we consider maximal cliques cliques that are not contained in any other clique. We use these to extract complex maximal cliques and subsequently mine these for interesting sets of object types (including complex types). That is, we mine interesting complex relationships. We show that applying the GLIMIT itemset mining algo- rithm to this task leads to far superior performance than using an Apriori style approach.