Sampling large database for association rules without candidate generationیازدهمین کنفرانس سالانه انجمن کامپیوتر ایران
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.<\div>
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