Abstract
Mining association rules is animportant area in data mining. Massively increasing volume of data in reallife databases has motivated researchers to design novel and efficientalgorithm for association rules mining. In this paper, we propose anassociation rule mining algorithm that integrates interestingness criteriaduring the process of building the model. One of the main features of thisapproach is to capture the user background knowledge, which is monotonicallyaugmented. We tested our algorithm and experiment with some public medicaldatasets and found the obtained results quite promising.
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Wasan, S.K., Bhatnagar, V., Kaur, H. (2007). An Efficient Interestingness based Algorithm for Mining Association Rules in Medical Databases. In: Elleithy, K. (eds) Advances and Innovations in Systems, Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6264-3_30
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DOI: https://doi.org/10.1007/978-1-4020-6264-3_30
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