Data-Driven Extraction of Association Rules of Dependent Abnormal Behaviour Groups

Abstract

This work proposes a methodology for identifying dependent abnormal behaviours through the extraction of association rules from data. The practical case considered makes use of a database of alarms generated by different supervision systems of the CERN (European Centre for Nuclear Research) technical infrastructure. The methodology is based on the representation of the alarm database with a binary matrix and the use of the Apriori algorithm for mining association rules. An application to a large-scale database of alarms generated by various monitoring systems of the point 8 of CERN is presented.