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Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases
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A hybrid interestingness heuristic approach for attribute-oriented mining
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
From data to global generalized knowledge
Decision Support Systems
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We present a hybrid heuristic algorithm, clusterAOI, that generates a more interesting generalised table than obtained via attribute-oriented induction (AOI). AOI tends to overgeneralise as it uses a fixed global static threshold to cluster and generalise attributes irrespective of their features, and does not evaluate intermediate interestingness. In contrast, clusterAOI uses attribute features to dynamically recalculate new attribute thresholds and applies heuristics to evaluate cluster quality and intermediate interestingness. Experimental results show improved interestingness, better output pattern distribution and expressiveness, and improved runtime.