DIVERSITY-BASED INTERESTINGNESS MEASURES FOR ASSOCIATION RULE MINING

Association rule interestingness measures are used to help select and rank association rule patterns. Diversity-based measures have been used to determine the relative interestingness of summaries. However, little work has been done that investigates diversity measures with association rule mining. Besides support, confidence, and lift, there are other interestingness measures, which include generality (also known as coverage), reliability, peculiarity, novelty, surprisingness, utility, and applicability. This paper investigates the application of diversity-based measures to association rule mining.

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