Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARN
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Mining Generalized Association Rules Using Pruning Techniques
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An alert reasoning method for intrusion detection system using attribute oriented induction
ICOIN'05 Proceedings of the 2005 international conference on Information Networking: convergence in broadband and mobile networking
A hybrid heuristic approach for attribute-oriented mining
Decision Support Systems
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A hybrid interestingness heuristic algorithm, clusterAOI, is presented that generates a more interesting generalized final table than traditional attribute-oriented induction (AOI). AOI uses a global static threshold to generalize attributes irrespective of attribute features, consequently leading to overgeneralisation. In contrast, clusterAOI uses attribute features such as concept hierarchies and distinct domain attribute values to dynamically recalculate new attribute thresholds for each of the less significant attributes. ClusterAOI then applies new heuristic functions and the Kullback-leibler (K-L) measure to evaluate interestingness for each attribute and then for all attributes by a harmonic aggregation in each generalisation iteration. The dynamic threshold adjustment, aggregation and evaluation of interestingness within each generalization iteration ultimately generates a higher quality final table than traditional AOI. Results from real-world cancer and population datasets show both significantly increased interestingness and better performance compared with AOI.