Spatial associative classification: propositional vs structural approach

In Spatial Data Mining, spatial dimension adds a substantial complexity to the data mining task. First, spatial objects are characterized by a geometrical representation and relative positioning with respect to a reference system, which implicitly define both spatial relationships and properties. Second, spatial phenomena are characterized by autocorrelation, i.e., observations of spatially distributed random variables are not location-independent. Third, spatial objects can be considered at different levels of abstraction (or granularity). The recently proposed SPADA algorithm deals with all these sources of complexity, but it offers a solution for the task of spatial association rules discovery. In this paper the problem of mining spatial classifiers is faced by building an associative classification framework on SPADA. We consider two alternative solutions for associative classification: a propositional and a structural method. In the former, SPADA obtains a propositional representation of training data even in spatial domains which are inherently non-propositional, thus allowing the application of traditional data mining algorithms. In the latter, the Bayesian framework is extended following a multi-relational data mining approach in order to cope with spatial classification tasks. Both methods are evaluated and compared on two real-world spatial datasets and results provide several empirical insights on them.