A spatial co-location pattern represents relationships between spatial features that are frequently located in close proximity to one another. Such a pattern is one of the most important concepts for geographic context awareness of ubiquitous Geographic Information System (GIS). We constructed a framework for co-location pattern mining using the transaction-based approach, which employs maximal cliques as a transaction-type dataset; we first define transaction-type data and verify that the definition satisfies the requirements, and we also propose an efficient way to generate all transaction-type data. The constructed framework can play a role as a theoretical methodology of co-location pattern mining, which supports geographic context awareness of ubiquitous GIS.
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