Comparative survey of association rule mining algorithms based on multiple-criteria decision analysis approach

Abstract

Mining association rules is a leading task, which attracted the attention of researchers, it is one of the technical potential of data mining that allows discovered correlations and association between voluminous datasets. It generally spend two important steps, in the first is the extraction of frequent items, and extracting association rules from this frequent items for the second step. This extraction is a difficult task, costly in terms of response time and memory space as the number of frequent items is exponential to the number of items in database. Many algorithms have been designed to answer these problems. Nevertheless, the high number of algorithms is itself an obstacle to the ability of choice of an expert. In this context we propose an approach to make a good choice of extraction algorithm based on multi-criteria analysis.