سال انتشار: ۱۳۸۶

محل انتشار: اولین کنفرانس داده کاوی ایران

تعداد صفحات: ۸

نویسنده(ها):

Mahmood Deypir – Department of computer Science and Engineering, Shiraz University Shiraz, Iran.
Mohammad Hadi Sadreddini – Department of computer Science and Engineering, Shiraz University Shiraz, Iran.

چکیده:

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.