期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2016
卷号：36
期号：1
页码：10-13
DOI：10.14445/22312803/IJCTT-V36P102
出版社：Seventh Sense Research Group
摘要：In recent years, a number of association rule mining algorithms like Apriori were developed, they are purely binary in nature. It doesn’t consider quantity and profit(profit per unit). In these algorithms, two important measures viz., support count and confidence were used to generate the frequent item sets and their corresponding association rules . But in reality, these two measures are not sufficient for decision making in terms of profitability. In this a weighted frame work has been discussed by taking into account the profit( intensity of the item) and the quantity of each item in each transaction of the given database. FP Growth algorithm is one of the best algorithm to generate frequent item sets, but it does not consider the profit as well as the quantity of items in the transactions of the database. Here we propose an algorithm FPWQ, which eliminates the disadvantages of frequent database scanning and it also considers quantity and profit per unit.. In this by incorporating the profit per unit and quantity measures we generates Weighted Frequent Itemsets (FPWFI) and corresponding Weighted Association Rules (FPWAR).
