期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：5
出版社：S.S. Mishra
摘要：This research work proposes an improved Apriori algorithm to minimize the number of candidate sets while generating association rules by evaluating quantitative information associated with each item that occurs in a transaction, which wa s usually, discarded as traditional association rules focus just on qualitative correlations. The proposed approach reduces not only the number of item sets generated but also the overall execution time of the algorithm. Any valued attribute will be treated as quantitative and will be used to derive the quantitative association rules which usually increases the rules 'information content. Transaction reduction is achieved by discardin g the transactions that does not contain any frequent item set in subsequent scans which in turn reduces overall execution time. Dynamic item set counting is done by adding new candidate item sets only when all of their subsets are estimated to be frequent. The frequent item ranges are the basis for generating higher order item ranges using Apriori algorithm. During each iteration of the algorithm, use the frequent sets from the previous iteration to generate the candidate sets and check whether their support is above the threshold. The set of candidate sets found is pruned by a strategy that discards sets which contain infrequent subsets. This work evaluates the scalability of the algorithm by considering transaction time, number of item sets used in the transaction and memory utilization. Quantitative association rules can be used in several domains where the traditional approach is employed. The unique requirement for such use is to have a semantic connection between the components of the item-value pairs..
