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...

During the development of many diseases such as cancer and diabetes, the pattern of gene expression within certain cells changes. A vital part of understanding these diseases will come from understanding the factors governing gene expression. This thesis work focused on mining association rules in the context of gene expression. We designed and developed a tool that enables domain experts to in...

Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first ...

This study integrates association rules analysis, a methodology for selecting potential interactions, with Poisson regression modeling. Though typically ignored in conventional Poisson regression, interactions are very common in practice. However, selecting a Poisson regression model when many main effects and interactions are involved is problematic. In this study, we develop a model selection...

This work extends fuzzy inference-grams (fingrams) to fuzzy association rules (FAR), yielding FARFingrams. Their analysis pays attention to interpretability issues. An important open problem in association rule mining is the huge number of frequent itemsets and interesting rules to uncover and communicate to the user. FAR-Fingrams address such problem through visual analysis. They ease the sele...

We present a theoretical analysis for prediction algorithms based on association rules. As part of this analysis, we introduce a problem for which rules are particularly natural, called “sequential event prediction.” In sequential event prediction, events in a sequence are revealed one by one, and the goal is to determine which event will next be revealed. The training set is a collection of pa...

This article introduces the concept of the variability of association rules of products through the estimate of a new indicator called overall variability of association rules (OCVR). The proposed indicator applied to super market chain products, tries to highlight product market baskets, with great variability in consumer behavior. Parameter of the variability of association rules in connectio...

A large database, such as POS data, could give us many insights about customer behavior. Many techniques and measures have been proposed to extract the interesting rules. As the study of Association rule mining has proceeded, the rules about items that are not bought together at the same transaction have been regarded as important. Although this concept, Negative Association rule mining, is qui...