Publications:Incremental classification of process data for anomaly detection based on similarity analysis

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Title Incremental classification of process data for anomaly detection based on similarity analysis
Author Stefan Byttner and Magnus Svensson and Gancho Vachkov
Year 2011
PublicationType Conference Paper
Journal
HostPublication EAIS 2011 : 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems : April 11-15, 2011, Paris, France
Conference Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011, Paris, France, 11 - 15 April 2011, Category number CFP1114N-ART, Code85920
DOI http://dx.doi.org/10.1109/EAIS.2011.5945928
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:404651
Abstract Performance evaluation and anomaly detection in complex systems are time consuming tasks based on analyzing, similarity analysis and classification of many different data sets from real operations. This paper presents an original computational technology for unsupervised incremental classification of large data sets by using a specially introduced similarity analysis method. First of all the so called compressed data models are obtained from the original large data sets by a newly proposed sequential clustering algorithm. Then the datasets are compared by pairs not directly, but by using their respective compressed data models. The evaluation of the pairs is done by a special similarity analysis method that uses the so called Intelligent Sensors (Agents) and data potentials. Finally a classification decision is generated by using a predefined threshold of similarity. The applicability of the proposed computational scheme for anomaly detection, based on many available large data sets is demonstrated on an example of 18 synthetic data sets. Suggestions for further improvements of the whole computation technology and a better applicability are also discussed in the paper.