期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：5
页码：2111-2115
出版社：TechScience Publications
摘要：Association rule mining is a key issue in data mining. However, the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes. In this paper, we introduce a new measure w-support, which does not require pre-assigned weights. It takes the quality of transactions into consideration using link-based models. A fast mining algorithm is given, and a large amount of experimental results are presented. The weights are completely derived from the internal structure of the database based on the Assumption that good transactions consist of good items. Consequently, some item sets, which are not so frequent but accompany good items, may easily be missed by traditional counting-based model but discovered by ours. The hits model and algorithm are used to derive the weights of transactions from a database with only binary attributes. Based on these weights, a new measure w-support is defined to give the significance of item sets. It differs from the traditional support in taking the quality of transactions into consideration. Then, the w-support and w-confidence of association rules are defined in analogy to the definition of support and confidence. An Apriori-like algorithm is proposed to extract association rules whose w-support and w-confidence are above some given thresholds., Re
关键词：wsupport; ranking association rules; HITS; link;analysisa..
