Rare Event Prediction

Rare events prediction is a very interesting and critical issue that has been approached within various contexts by research areas, such as statistics and machine learning. Data mining has provided a set of tools to treat this problem when the size as well as the inherent features of the data, such as noise, randomness and special data types, become an issue for the traditional methods. Transaction databases that contain sets of events require special approaches in order to extract valuable temporal knowledge. Sequential analysis aims to discover patterns or rules describing the temporal structure of data.

Contact Person(s)

For further information please contact:

Berberidis Christos

Academic Associate, School of Science and Technology

Tel: +30 2310 807534

Selected Publications

  • Berberidis C. and Vlahavas P. I., "Detection And Prediction Of Rare Events In Transaction Databases", International Journal of Artificial Intelligence Tools (IJAIT), World Scientific, 16(5), pp. 829 – 848, 2007 (2007) • Journal Paper
  • Berberidis C., Angelis L. and Vlahavas P. I., "Inter-transaction Association Rules Mining for Rare Events Prediction", Proc. (companion volume) 3rd Hellenic Conference on Artificial Intelligence (SETN '04), Samos, Greece, 2004 (2004) • Conference Paper
  • Berberidis C., Angelis L. and Vlahavas P. I., "PREVENT: An algorithm for mining inter-transactional patterns for the prediction of rare events", Proc. 2nd European Starting AI Researcher Symposium (STAIRS' 04), IOS Press, Valencia, Spain, 23-24 August 2004 (2004) • Conference Paper

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