Mining sequential patterns only considers the sequential purchasing behaviors for most of the consumers. We can use this information to predict what products the consumers would like to purchase next time, but we cannot use this information to predict when the consumer will need the products in the future, that is we cannot know when the products need to be promoted to the consumers. In order t...

Association rule algorithms can produce a very large number of output patterns. This has raised questions of whether the set of discovered rules \over t" the data because all the patterns that satisfy some constraints are generated (the Bonferroni e ect). In other words, the question is whether some of the rules are \false discoveries" that are not statistically signi cant. We present a novel a...

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...

Association rules is one of data mining methods for discovering knowledge from large amounts of data in databases. In this paper, we propose an intelligent method for discovering association rules, called IMAR. IMAR is designed through three main phases, i.e., preprocessing, processing and post processing. It has been experimented using three domain data sets, i.e., Australian Credit Card (ACC)...

Data mining has emerged to address the problem of transforming data into useful knowledge. Although most data mining techniques, such as the use of association rules, may substantially reduce the search effort over large data sets, often, the consequential outcomes surpass the amount of information humanly manageable. On the other hand, important association rules may be overlooked owing to the...