Mining association rules associates events that took place together. In market basket analysis, these discovered rules associate items purchased together. Items that are not part of a transaction are not considered. In other words, typical association rules do not take into account items that are part of the domain but that are not together part of a transaction. Association rules are based on ...

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...

Association rules present one of the most versatile techniques for the analysis of binary data, with applications in areas as diverse as retail, bioinformatics, and sociology. In this chapter, the origin of association rules is discussed along with the functions by which association rules are traditionally characterised. Following the formal definition of an association rule, these functions – ...

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 extensions,...

In many online shopping applications, traditional Association Rule (AR) mining has limitations as it only deals with the items that are sold but ignores the items that are almost sold. For example, those items that are put into the basket but not checked out. We say that those almost sold items carry hesitation information since customers are hesitating to buy them. The hesitation information o...

Data envelopment analysis (DEA) is a relatively new data oriented approach to evaluate performance of a set of peer entities called decision-making units (DMUs) that convert multiple inputs into multiple outputs. Within a relative limited period, DEA has been converted into a strong quantitative and analytical tool to measure and evaluate performance. In an article written by Toloo et al. (2009...

Discovering association rules and association rules change (ARC) from existing large databases is an important problem. This paper presents an approach based on multi-hash chain structures to mine association rules change from large database with shorter transactions. In most existing algorithms of association rules change, the mining procedure is divided into two phases, first, association rul...