On Genetic Algorithms for Detecting Frequent Item Sets And Large Bite Sets

摘要

This paper introduces the use of genetic algorithms to mine binary datasets for obtaining frequent item sets and large bite item sets, two classes of problems that are important for optimal exposure of item sets to customers and for efficient advertising campaigns. Whereas both problems can be approached in a common framework, we highlight specific features of the fitness functions suitable for each of the problems.