@Article{          geng.ea2006-interestingness,
    author       = {Geng, Liqiang and Hamilton, Howard J.},
    year         = {2006},
    title        = {Interestingness measures for data mining: A Survey},
    abstract     = {Interestingness measures play an important role in data
                   mining, regardless of the kind of patterns being mined.
                   These measures are intended for selecting and ranking
                   patterns according to their potential interest to the
                   user. Good measures also allow the time and space costs of
                   the mining process to be reduced. This survey reviews the
                   interestingness measures for rules and summaries,
                   classifies them from several perspectives, compares their
                   properties, identifies their roles in the data mining
                   process, gives strategies for selecting appropriate
                   measures for applications, and identifies opportunities
                   for future research in this area. {\textcopyright} 2006
                   ACM.},
    doi          = {10.1145/1132960.1132963},
    issn         = {0360-0300},
    journal      = {ACM Computing Surveys},
    keywords     = {Association rules,Classification rules,Interest
                   measures,Interestingness measures,Knowledge
                   discovery,Summaries,computer},
    mendeley-tags= {computer},
    month        = {sep},
    number       = {3},
    pages        = {9},
    url          = {https://dl.acm.org/doi/10.1145/1132960.1132963},
    volume       = {38}
}

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