  The discovery of new and interesting patterns in large datasets, known as
data mining, draws more and more interest as the quantities of available data
are exploding. Data mining techniques may be applied to different domains and
fields such as computer science, health sector, insurances, homeland security,
banking and finance, etc. In this paper we are interested by the discovery of a
specific category of patterns, known as rare and non-present patterns. We
present a novel approach towards the discovery of non-present patterns using
rare item-set mining.
