Abstract
Driven by the major adoption of mobile devices, user contributed geographic information has become ubiquitous. A typical example is georeferenced and tagged social media, linking a location to a set of features or attributes. Mining frequent sets of discrete attributes to discover interesting patterns and rules of attribute usage in such data sets is an important data mining task.
In this work we extend the frequent itemset mining framework to model the spatial distribution of itemsets and association rules. For this, we expect the input transactions to have an associated spatial attribute, as, for example, present in georeferenced tag sets. Using the framework, we formulate interestingness measures that are based on the underlying spatial distribution of the input transactions, namely area, spatial support, location-conditional support, and spatial confidence. We show that describing the spatial characteristics of itemsets cannot be handled by existing approaches to mine association rules with numeric attributes, and that the problem is different from co-location pattern mining and spatial association rules mining. We demonstrate the usefulness of our proposed extension by different mining tasks using a real-world data set from Flickr.
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Sengstock, C., Gertz, M. (2013). Spatial Itemset Mining: A Framework to Explore Itemsets in Geographic Space. In: Catania, B., Guerrini, G., Pokorný, J. (eds) Advances in Databases and Information Systems. ADBIS 2013. Lecture Notes in Computer Science, vol 8133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40683-6_12
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DOI: https://doi.org/10.1007/978-3-642-40683-6_12
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