Association rules mining with three-dimensional data structure

Evgenia O. Khramshina, Alexander V. Prutzkow

Abstract


 Association rules mining progresses year by year. There are many algorithms of association rules mining. The most popular are the Apriori algorithm and the FP-Growth algorithm. But these algorithms have disadvantages. The Apriori algorithm requires many transaction base passes. The FP-Growth algorithm uses a many-edged (non-binary) tree data structure. Algorithm is characterized by the data structure used in it. We discover an association rules mining algorithm using three-dimensional data structure. Algorithm needs only two transaction base passes. The first pass is to insert transactions in three-dimensional data structure. The second pass is to count support of extracted from three-dimensional data structure itemsets. The algorithm tested and compared with the Apriori algorithm and the FP-Growth algorithm. The algorithm is more effective by memory usage than the FP-Growth algorithm, when number of unique elements is between 10 and 868, and the Apriori algorithm, when number of unique elements is between 49 and 498.


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