Carrascal, AlbertoDíez, AlbertoAzpeitia, Ander2024-07-242024-07-242009Carrascal , A , Díez , A & Azpeitia , A 2009 , Unsupervised methods for anomalies detection through intelligent monitoring systems . in Hybrid Artificial Intelligence Systems - 4th International Conference, HAIS 2009, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 5572 LNAI , pp. 137-144 , 4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009 , Salamanca , Spain , 10/06/09 . https://doi.org/10.1007/978-3-642-02319-4_17conference364202318597836420231870302-9743https://hdl.handle.net/11556/2004The success of intelligent diagnosis systems normally depends on the knowledge about the failures present on monitored systems. This knowledge can be modelled in several ways, such as by means of rules or probabilistic models. These models are validated by checking the system output fit to the input in a supervised way. However, when there is no such knowledge or when it is hard to obtain a model of it, it is alternatively possible to use an unsupervised method to detect anomalies and failures. Different unsupervised methods (HCL, K-Means, SOM) have been used in present work to identify abnormal behaviours on the system being monitored. This approach has been tested into a real-world monitored system related to the railway domain, and the results show how it is possible to successfully identify new abnormal system behaviours beyond those previously modelled well-known problems.8enginfo:eu-repo/semantics/openAccessconference output10.1007/978-3-642-02319-4_17ClusteringIntelligent Monitoring SystemsUnsupervised Anomaly DetectionUnsupervised ClassificationTheoretical Computer ScienceGeneral Computer Sciencehttp://www.scopus.com/inward/record.url?scp=70350646774&partnerID=8YFLogxK