@misc{12803,
  abstract     = {{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.}},
  author       = {{Wunderlich, Paul and Hranisavljevic, Nemanja}},
  booktitle    = {{2019 IEEE 17th International Conference on Industrial Informatics (INDIN)}},
  isbn         = {{978-1-7281-2928-0}},
  keywords     = {{probabilistic graphical model, causal model, alarm flood reduction, Bayesian network, Markov chain, restricted boltzmann machine, automata}},
  location     = {{Helsinki, Finland }},
  pages        = {{1285--1290}},
  publisher    = {{IEEE}},
  title        = {{{Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction}}},
  doi          = {{10.1109/indin41052.2019.8972251}},
  year         = {{2019}},
}

@inproceedings{2088,
  abstract     = {{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.

In 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.}},
  author       = {{Niggemann, Oliver and Lohweg, Volker and Tack, Tim}},
  booktitle    = {{33rd Annual German Conference on Artificial Intelligence (KI 2010)}},
  isbn         = {{978-3-642-16110-0}},
  keywords     = {{Markov Chain, Cluster Algorithm, Edge Weight, Spectral Cluster, Stable Distribution}},
  pages        = {{184--194}},
  publisher    = {{Springer}},
  title        = {{{A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs}}},
  doi          = {{https://doi.org/10.1007/978-3-642-16111-7_21}},
  volume       = {{6359}},
  year         = {{2010}},
}