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Standardizing interestingness measures for association rules
Author(s) -
Shaikh Mateen R.,
McNicholas Paul D.,
Antonie M. Luiza,
Murphy Thomas Brendan
Publication year - 2018
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11394
Subject(s) - measure (data warehouse) , computer science , association rule learning , range (aeronautics) , data mining , value (mathematics) , association (psychology) , machine learning , psychology , psychotherapist , materials science , composite material
Interestingness measures provide information about association rules. The value of an interestingness measure is often interpreted relative to the overall range of the interestingness measure. However, properties of individual association rules can further restrict what value an interestingness measure can achieve. These additional constraints are not typically taken into account in analysis, potentially misleading the investigator. Considering the value of an interestingness measure relative to this further constrained range provides greater insight than the original range alone and can even alter researchers' impressions of the data. Standardizing interestingness measures takes these additional restrictions into account, resulting in values that provide a relative measure of the attainable values. We explore the impacts of standardizing interestingness measures on real and simulated data.