A clustering-based visualization of spatial patterns

Extraction of interesting colocations in georeferenced data is one of the major tasks in spatial pattern mining. Considering a set of spatial Boolean features, the goal is to find relevant subsets of features associated with objects often located together. In this context, the main drawback is the interpretation of extracted patterns by domain experts. Indeed, common textual representation of colocations loses important spatial information. To overcome this problem, we propose a new clustering-based visualization technique deeply integrated in the colocation algorithm. This new simple, concise and intuitive cartographic representation consider both spatial information and experts practice. The whole process has been experimented on a real-world geological data set and the addedvalue of the method confirmed by domain experts.

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