Parallel Mining of All None-Derivable Frequent Itemsets
محل انتشار: اولین کنفرانس داده کاوی ایران
سال انتشار: 1386
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,916
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شناسه ملی سند علمی:
IDMC01_106
تاریخ نمایه سازی: 20 خرداد 1386
چکیده مقاله:
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.
کلیدواژه ها:
نویسندگان
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.