Découverte de règles de fouille de motifs dans les bases de données transactionnelles

Abstract

A major problem in data mining is High Utility Pattern Mining (HUPM), which seeks to find combinations of items that have a significant impact on a specific metric, such as sales, profits or customer satisfaction. Due to the growth in the volume of data in the field of Big Data, it is essential to design efficient algorithms to quickly extract these sets of high-value elements. In our study, we address the topic of finding high utility patterns in real transactional data bases. The objective is to discover very useful patterns in these bases. The utility of an item in the database represents its importance in relation to other items ; it can often be associated with the price of the item, but can also be defined by other criteria. Two algorithms were tested and applied on two real bases : the first from a pharmacy and the second containing purchases made in a fruit shop. This allows to extract two different forms of high-utility patterns : High Utility Itemsets (HUIs) and High Utility Association Rules (HARs)