[{"publisher":"IEEE","abstract":[{"lang":"eng","text":"The increasing amount of alarms and information for an operator in a modern plant becomes a significant safety risk. Although the notifications are a valuable support, they also lead to the curse of overloading with information for the operator. Due to the huge amount of alarms it is almost impossible to separate the crucial information from the insignificant ones. Therefore, new procedures are required to reduce these alarm floods and support the operator to minimize the safety risk. One approach is based on learning a causal model that represents the relationships between the alarms. This allows alarm sequences that are causally implied to be reduced to the root cause alarm. Fundamental element of this approach is the causal model. Therefore in this work, different probabilistic graphical models are considered and evaluated on the basis of appropriate criteria. A real use case of a bottle filling module serves as a benchmark for how well they are suitable as a causal model for the application in alarm flood reduction."}],"department":[{"_id":"DEP5023"}],"conference":{"location":"Helsinki, Finland ","name":"17th IEEE International Conference on Industrial Informatics (INDIN)","end_date":"2019-07-25","start_date":"2019-07-22"},"publication_status":"published","_id":"12803","doi":"10.1109/indin41052.2019.8972251","place":"[Piscataway, NJ]","user_id":"83781","type":"conference_editor_article","status":"public","page":"1285-1290","year":"2019","author":[{"id":"52317","first_name":"Paul","full_name":"Wunderlich, Paul","last_name":"Wunderlich"},{"last_name":"Hranisavljevic","id":"62376","full_name":"Hranisavljevic, Nemanja","first_name":"Nemanja"}],"citation":{"apa":"Wunderlich, P., &#38; Hranisavljevic, N. (2019). Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction. In Aalto University, Finland, Tampere University, Finland, &#38; Finnish Society of Automation, Finland (Eds.), <i>2019 IEEE 17th International Conference on Industrial Informatics (INDIN)</i> (pp. 1285–1290). IEEE. <a href=\"https://doi.org/10.1109/indin41052.2019.8972251\">https://doi.org/10.1109/indin41052.2019.8972251</a>","chicago":"Wunderlich, Paul, and Nemanja Hranisavljevic. <i>Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction</i>. Edited by Aalto University, Finland, Tampere University, Finland, and Finnish Society of Automation, Finland. <i>2019 IEEE 17th International Conference on Industrial Informatics (INDIN)</i>. [Piscataway, NJ]: IEEE, 2019. <a href=\"https://doi.org/10.1109/indin41052.2019.8972251\">https://doi.org/10.1109/indin41052.2019.8972251</a>.","van":"Wunderlich P, Hranisavljevic N. Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction. Aalto University, Finland, Tampere University, Finland, Finnish Society of Automation, Finland, editors. 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). [Piscataway, NJ]: IEEE; 2019.","ufg":"<b>Wunderlich, Paul/Hranisavljevic, Nemanja</b>: Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction, hg. von Aalto University, Finland/Tampere University, Finland, Finnish Society of Automation, Finland, [Piscataway, NJ] 2019.","mla":"Wunderlich, Paul, and Nemanja Hranisavljevic. “Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction.” <i>2019 IEEE 17th International Conference on Industrial Informatics (INDIN)</i>, edited by Aalto University, Finland et al., IEEE, 2019, pp. 1285–90, <a href=\"https://doi.org/10.1109/indin41052.2019.8972251\">https://doi.org/10.1109/indin41052.2019.8972251</a>.","havard":"P. Wunderlich, N. Hranisavljevic, Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction, IEEE, [Piscataway, NJ], 2019.","bjps":"<b>Wunderlich P and Hranisavljevic N</b> (2019) <i>Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction</i>, Aalto University, Finland, Tampere University, Finland, and Finnish Society of Automation, Finland (eds). [Piscataway, NJ]: IEEE.","short":"P. Wunderlich, N. Hranisavljevic, Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction, IEEE, [Piscataway, NJ], 2019.","chicago-de":"Wunderlich, Paul und Nemanja Hranisavljevic. 2019. <i>Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction</i>. Hg. von Aalto University, Finland, Tampere University, Finland, und Finnish Society of Automation, Finland. <i>2019 IEEE 17th International Conference on Industrial Informatics (INDIN)</i>. [Piscataway, NJ]: IEEE. doi:<a href=\"https://doi.org/10.1109/indin41052.2019.8972251\">10.1109/indin41052.2019.8972251</a>, .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Wunderlich, Paul</span> ; <span style=\"font-variant:small-caps;\">Hranisavljevic, Nemanja</span> ; <span style=\"font-variant:small-caps;\">Aalto University, Finland</span> ; <span style=\"font-variant:small-caps;\">Tampere University, Finland</span> ; <span style=\"font-variant:small-caps;\">Finnish Society of Automation, Finland</span> (Hrsg.): <i>Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction</i>. [Piscataway, NJ] : IEEE, 2019","ieee":"P. Wunderlich and N. Hranisavljevic, <i>Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction</i>. [Piscataway, NJ]: IEEE, 2019, pp. 1285–1290. doi: <a href=\"https://doi.org/10.1109/indin41052.2019.8972251\">10.1109/indin41052.2019.8972251</a>.","ama":"Wunderlich P, Hranisavljevic N. <i>Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction</i>. (Aalto University, Finland, Tampere University, Finland, Finnish Society of Automation, Finland, eds.). IEEE; 2019:1285-1290. doi:<a href=\"https://doi.org/10.1109/indin41052.2019.8972251\">10.1109/indin41052.2019.8972251</a>"},"title":"Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction","keyword":["probabilistic graphical model","causal model","alarm flood reduction","Bayesian network","Markov chain","restricted boltzmann machine","automata"],"language":[{"iso":"eng"}],"corporate_editor":["Aalto University, Finland","Tampere University, Finland","Finnish Society of Automation, Finland"],"date_updated":"2025-06-26T13:38:06Z","publication_identifier":{"isbn":["978-1-7281-2928-0"],"eisbn":["978-1-7281-2927-3"]},"publication":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","date_created":"2025-04-16T06:29:04Z"},{"place":"Berlin","_id":"2088","doi":"https://doi.org/10.1007/978-3-642-16111-7_21","user_id":"45673","intvolume":"      6359","type":"conference","publisher":"Springer","department":[{"_id":"DEP5023"}],"abstract":[{"lang":"eng","text":"Clustering remains a major topic in machine learning; it is used e.g. for document categorization, for data mining, and for image analysis. In all these application areas, clustering algorithms try to identify groups of related data in large data sets.\r\n\r\nIn this paper, the established clustering algorithm MajorClust ([12]) is improved; making it applicable to data sets with few structure on the local scale—so called near-homogeneous graphs. This new algorithm MCProb is verified empirically using the problem of image clustering. Furthermore, MCProb is analyzed theoretically. For the applications examined so-far, MCProb outperforms other established clustering techniques."}],"publication_status":"published","series_title":"Lecture Notes in Computer Science","keyword":["Markov Chain","Cluster Algorithm","Edge Weight","Spectral Cluster","Stable Distribution"],"language":[{"iso":"eng"}],"date_updated":"2023-03-15T13:49:38Z","publication":"33rd Annual German Conference on Artificial Intelligence (KI 2010)","publication_identifier":{"eisbn":["978-3-642-16111-7"],"isbn":["978-3-642-16110-0"]},"date_created":"2019-12-02T08:21:26Z","status":"public","page":"184-194","year":2010,"author":[{"first_name":"Oliver","last_name":"Niggemann","id":"10876","full_name":"Niggemann, Oliver"},{"last_name":"Lohweg","first_name":"Volker","full_name":"Lohweg, Volker","id":"1804","orcid":"0000-0002-3325-7887"},{"full_name":"Tack, Tim","first_name":"Tim","last_name":"Tack"}],"main_file_link":[{"url":"https://link.springer.com/chapter/10.1007/978-3-642-16111-7_21"}],"volume":6359,"citation":{"ieee":"O. Niggemann, V. Lohweg, and T. Tack, “A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs,” in <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, 2010, vol. 6359, pp. 184–194.","ama":"Niggemann O, Lohweg V, Tack T. A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>. Vol 6359. Lecture Notes in Computer Science. Berlin: Springer; 2010:184-194. doi:<a href=\"https://doi.org/10.1007/978-3-642-16111-7_21\">https://doi.org/10.1007/978-3-642-16111-7_21</a>","short":"O. Niggemann, V. Lohweg, T. Tack, in: 33rd Annual German Conference on Artificial Intelligence (KI 2010), Springer, Berlin, 2010, pp. 184–194.","chicago-de":"Niggemann, Oliver, Volker Lohweg und Tim Tack. 2010. A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, 6359:184–194. Lecture Notes in Computer Science. Berlin: Springer. doi:<a href=\"https://doi.org/10.1007/978-3-642-16111-7_21,\">https://doi.org/10.1007/978-3-642-16111-7_21,</a> .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Niggemann, Oliver</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span> ; <span style=\"font-variant:small-caps;\">Tack, Tim</span>: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, <i>Lecture Notes in Computer Science</i>. Bd. 6359. Berlin : Springer, 2010, S. 184–194","apa":"Niggemann, O., Lohweg, V., &#38; Tack, T. (2010). A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i> (Vol. 6359, pp. 184–194). Berlin: Springer. <a href=\"https://doi.org/10.1007/978-3-642-16111-7_21\">https://doi.org/10.1007/978-3-642-16111-7_21</a>","chicago":"Niggemann, Oliver, Volker Lohweg, and Tim Tack. “A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs.” In <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, 6359:184–94. Lecture Notes in Computer Science. Berlin: Springer, 2010. <a href=\"https://doi.org/10.1007/978-3-642-16111-7_21\">https://doi.org/10.1007/978-3-642-16111-7_21</a>.","van":"Niggemann O, Lohweg V, Tack T. A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: 33rd Annual German Conference on Artificial Intelligence (KI 2010). Berlin: Springer; 2010. p. 184–94. (Lecture Notes in Computer Science; vol. 6359).","mla":"Niggemann, Oliver, et al. “A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs.” <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, vol. 6359, Springer, 2010, pp. 184–94, doi:<a href=\"https://doi.org/10.1007/978-3-642-16111-7_21\">https://doi.org/10.1007/978-3-642-16111-7_21</a>.","bjps":"<b>Niggemann O, Lohweg V and Tack T</b> (2010) A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i>, vol. 6359. Berlin: Springer, pp. 184–194.","havard":"O. Niggemann, V. Lohweg, T. Tack, A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs, in: 33rd Annual German Conference on Artificial Intelligence (KI 2010), Springer, Berlin, 2010: pp. 184–194.","ufg":"<b>Niggemann, Oliver et. al. (2010)</b>: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs, in: <i>33rd Annual German Conference on Artificial Intelligence (KI 2010)</i> (=<i>Lecture Notes in Computer Science 6359</i>), Berlin, S. 184–194."},"title":"A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs"}]