Notice of RetractionStudy of high motivation itemsets mining

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Algorithms for support-based association rules mining can only discover frequent itemsets, can not discover the non-frequent itemsets with high utility values; utility-based association rules mining aims at discovering high utility itemsets, without considering the itemsets whose utility values are not high but the product of the support and utility of the same itemset is very large. To solve the problem, we propose a new measure, i.e., motivation, to measure the importance of an itemset and a down-top algorithm called HM-Two-Phase-Miner to discover high motivation itemsets. Motivation integrates the advantages of support and utility, and thus can reflect both the semantic significance and statistical significance of an itemset. In HM-Two-Phase-Miner algorithm, transaction-weighted motivation downward closure property is adopted to cut down the search space.