Keywords: Association rules are rules of the kind "70% of the customers who buy vine and cheese also buy grapes". While the traditional field of application is market basket analysis, association rule mining has been applied to various fields since then, which has led to a number of important modifications and extensions. We discuss the most frequently applied approach that is central to many e...

The Data Mining process enables the end users to analyse, understand and use the extracted knowledge in an intelligent system or to support in the decision-making processes. However, many algorithms used in the process encounter large quantities of patterns, complicating the analysis of the patterns. This fact occurs with association rules, a Data Mining technique that tries to identify intrins...

We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. For example, given a taxonomy that says that jackets is-a outerwear is-e clothes, we may infer a rule that “people who buy outerw...

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...