Top-K interesting preference rules mining based on MaxClique

作者：

Highlights：

• We devise a MPM algorithm for mining contextual preference rules of individual user.

• We construct a new belief system by the aggregation algorithm RSA.

• We propose a novelty preference interestingness measure PIM to evaluate the quality of preference rules.

摘要

•We devise a MPM algorithm for mining contextual preference rules of individual user.•We construct a new belief system by the aggregation algorithm RSA.•We propose a novelty preference interestingness measure PIM to evaluate the quality of preference rules.

论文关键词：Maxclique,Association rules,Conditional preference rules,Belief system,Interestingness measure

论文评审过程：Received 4 September 2018, Revised 2 October 2019, Accepted 17 October 2019, Available online 8 November 2019, Version of Record 11 November 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113043