MULTILEVEL ASSOCIATION RULE MINING FOR LARGE DATASETS: A REVIEW

Minal Devidas Vanarse, Smita Kasar

Abstract


Association rule mining is an imperative research issue in domain of data mining, but association rules mining at single concept level lead to uninteresting rules. For large data applications, it is hard to discover solid association rules among data elements at single abstraction level, because of the lack of data in multidimensional space. So finding association rules at multiple abstraction levels leads to knowledge discovery. The discovery of association rules at multiple levels is helpful in numerous applications. Prior work in field of data mining has yielded proficient techniques for finding multilevel rules. This study aims to review the multilevel association rule mining and different techniques used for mining multilevel association rules from large datasets.

Keywords


Multilevel Association; Rule Mining; Apriori; Genetic Algorithm; Particle Swarm Optimization.

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DOI: https://doi.org/10.26483/ijarcs.v8i8.4793

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