Pattern Detection with Rare Item-set Mining
This work addresses pattern detection for applications in domains like computer science and health, but appears incremental as it focuses on a specific category within existing data mining techniques.
The paper tackles the problem of discovering rare and non-present patterns in large datasets by introducing a novel approach using rare item-set mining, but does not provide concrete numerical results.
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.