Pruning Statistically Insignificant Association Rules in the Presence of High-confidence Rules in Web Usage Data | Zendy

Maja Dimitrijević | Zendy; Zita Bošnjak | Zendy

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Pruning Statistically Insignificant Association Rules in the Presence of High-confidence Rules in Web Usage Data

Author(s) -

Maja Dimitrijević,

Zita Bošnjak

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.08.107

Subject(s) - computer science , association rule learning , pruning , data mining , web application , web mining , information retrieval , web page , world wide web , agronomy , biology

Automatic discovery of web usage association rules is commonly used to extract the knowledge about web site visitors’ interests. Its drawback is the generation of too many not truly interesting rules that have high statistical interestingness measures. We propose a method to prune rules that are statistically insignificant with respect to more general rules. Such rules may exist in the presence of high-confidence rules, which is often the case in web usage data. The method effectiveness is validated on two real-life web usage data sets

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