سال انتشار: ۱۳۸۴

محل انتشار: یازدهمین کنفرانس سالانه انجمن کامپیوتر ایران

تعداد صفحات: ۴

نویسنده(ها):

Mahmood Deypir – Department of Computer Science and Engineering School of Engineering, Shiraz UniversityShiraz, Iran.
Mohammad Hadi Sadreddini –

چکیده:

In this study a new and fast approach is presented for mining association rule. It is a hybrid approach based on sampling algorithm which uses FP-Growth to find frequent itemset in sample data. This method neglect candidate generation and test due to FP-Tree projection of sample data. For the main memory limitation problem i.e. loading sample data and representing FPTree structure a useful technique is proposed. Both theoretical and experimental evaluations show that the approach is faster than Apriori-based sampling by orders of magnitude. In addition experimental evaluation shows that the approach is more efficient when the minimum support threshold is reduced.