An improved data mining approach using predictive itemsets

作者：Hong Tzung Pei^*; Horng Chyan Yuan; Wu Chih Hung; Wang Shyue Liang

来源：Expert Systems with Applications, 2009, 36(1): 72-80.

DOI：10.1016/j.eswa.2007.09.009

• Data mining
• Association rule
• Predictive itemset
• Data dependency
• Predicting minimum support

In this paper, we present a mining algorithm to improve the efficiency of. finding large itemsets. Based on the concept of prediction proposed in the (n, p) algorithm, our method considers the data dependency in the given transactions to predict promising and non-promising candidate itemsets. Our method estimates for each level a different support threshold that is derived from a data dependency parameter and determines whether an item should be included in a promising candidate itemset directly. In this way, we maintain the efficiency of. finding large itemsets by reducing the number of scanning the input dataset and the number candidate items. Experimental results show our method has a better efficiency than the apriori and the (n, p) algorithms when the minimum support value is small.

• 出版日期2009-1
• 单位中国人民解放军信息工程大学