A new algorithm for faster mining of generalized association rules

Generalized association rules as introduced in [9] and [5] are a very important extension of the so called simple or boolean association rules. Unfortunately, with current approaches mining generalized rules is computationally very expensive because there are many more frequent generalized itemsets than simple ones. We present and discuss strengths and weaknesses of known approaches to generate simple frequent itemsets, in particular with respect to their extensibility to generalized association rules. Based on both insights gained from this discussion and practical experiences we derive a new algorithm, called Prutax, to mine generalized frequent itemsets. The basic ideas of the algorithm and further optimisation are described. Experiments with both synthetic data and a real dataset show that Prutax is an order of magnitude faster than previous approaches. (orig.)SIGLEAvailable from TIB Hannover: RR 4367(98-4) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman