Parallel Mining of All None-Derivable Frequent Itemsets

Mining non-derivable frequent itemsets (NDIs) is one of the successful approaches to construct a concise representation of frequent patterns which is useful to generate smaller and more understandable rule set. Breadth-first and depth-first algorithms are the two main algorithms that have so far been proposed in the literature for mining non-derivable frequent itemsets. In this study parallel mining of all non-derivable frequent itemsets on the share-nothing parallel systems is investigated. A parallel algorithm called PNDI is proposed and implemented here. This algorithm parallelizes not only I/O costs but also computation cost of deduction rules evaluation. Experimental results on real-life datasets show that the parallel algorithm has fine speed up, scale up and size up.

Keywords:

Association Rules , None-derivable frequent itemsets , Parallel Data Mining