Résumé:
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)
