Discovering association rules (AR) among items in a large database is an important database mining problem. The number of association rules may be large. To alleviate this problem, we introduced in [1] a notion of representative association rules (RR). RR is a least set of rules that covers all association rules. The association rules, which are not representative ones, may be generated by mean...

Many business organizations generate a huge amount of transaction data. Association rule mining is a powerful analysis tool to extract the useful meanings and associations from large databases and many automated systems have been developed for mining association rules. However, most of these systems usually mine many association rules from large databases and it is not easy for a user to extrac...

Previous approaches for mining association rules generate large sets of association rules. Such sets are difficult for users to understand and manage. Here, the concept of a restricted conditional probability distribution is used to explain an association rule. Based on this concept, a new type of association rules, called basic association rules, is defined. We propose the GenBR algorithm to g...

due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. association rules mining is one of the data mining techniques to extract useful patterns in the form of association rules. one of the main problems in applying this technique on databases is the disclosure of sensitive data by endangering security and privacy. hiding the as...

Association rule mining is one of the most important and well-researched techniques of data mining, since the seminal papers by R. Agrawal et al. [1, 2]. It aims to induce associations among sets of items in the transaction databases or other data repositories. Ever since, several algorithms for specialized association tasks have appeared: quantitative association rules, generalized association...

Association Rule Mining means discovering interesting patterns with in large databases. Association rules are used in many application areas such as market base analysis, web log analysis, protein substructures. Several post processing methods were developed to reduce the number of rules using nonredundant rules or pruning techniques such as pruning, summarizing, grouping or visualization based...