A new algorithm for faster mining of generalized association rules View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1998

AUTHORS

Jochen Hipp , Andreas Myka , Rüdiger Wirth , Ulrich Güntzer

ABSTRACT

Generalized association rules are a very important extension of boolean association rules, but with current approaches mining generalized rules is computationally very expensive. Especially when considering the rule generation as being part of an interactive KDD-process this becomes annoying. In this paper we discuss strengths and weaknesses of known approaches to generate frequent itemsets. Based on the insights 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 and real-life data show that Prutax is an order of magnitude faster than previous approaches. More... »

PAGES

74-82

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0094807

DOI

http://dx.doi.org/10.1007/bfb0094807

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039337632


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