---
_id: '12803'
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.
author:
- first_name: Paul
full_name: Wunderlich, Paul
id: '52317'
last_name: Wunderlich
- first_name: Nemanja
full_name: Hranisavljevic, Nemanja
id: '62376'
last_name: Hranisavljevic
citation:
ama: 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, eds.). IEEE;
2019:1285-1290. doi:10.1109/indin41052.2019.8972251
apa: Wunderlich, P., & Hranisavljevic, N. (2019). Comparison of Different Probabilistic
Graphical Models as Causal Models in Alarm Flood Reduction. In Aalto University,
Finland, Tampere University, Finland, & Finnish Society of Automation, Finland
(Eds.), 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
(pp. 1285–1290). IEEE. https://doi.org/10.1109/indin41052.2019.8972251
bjps: 'Wunderlich P and Hranisavljevic N (2019) Comparison of Different
Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction,
Aalto University, Finland, Tampere University, Finland, and Finnish Society of
Automation, Finland (eds). [Piscataway, NJ]: IEEE.'
chicago: 'Wunderlich, Paul, and Nemanja Hranisavljevic. Comparison of Different
Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction.
Edited by Aalto University, Finland, Tampere University, Finland, and Finnish
Society of Automation, Finland. 2019 IEEE 17th International Conference on
Industrial Informatics (INDIN). [Piscataway, NJ]: IEEE, 2019. https://doi.org/10.1109/indin41052.2019.8972251.'
chicago-de: 'Wunderlich, Paul und Nemanja Hranisavljevic. 2019. Comparison of
Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction.
Hg. von Aalto University, Finland, Tampere University, Finland, und Finnish Society
of Automation, Finland. 2019 IEEE 17th International Conference on Industrial
Informatics (INDIN). [Piscataway, NJ]: IEEE. doi:10.1109/indin41052.2019.8972251,
.'
din1505-2-1: 'Wunderlich, Paul ; Hranisavljevic, Nemanja ; Aalto
University, Finland ; Tampere University,
Finland ; Finnish Society of Automation,
Finland (Hrsg.): Comparison of Different Probabilistic Graphical Models
as Causal Models in Alarm Flood Reduction. [Piscataway, NJ] : IEEE, 2019'
havard: P. Wunderlich, N. Hranisavljevic, Comparison of Different Probabilistic
Graphical Models as Causal Models in Alarm Flood Reduction, IEEE, [Piscataway,
NJ], 2019.
ieee: 'P. Wunderlich and N. Hranisavljevic, Comparison of Different Probabilistic
Graphical Models as Causal Models in Alarm Flood Reduction. [Piscataway, NJ]:
IEEE, 2019, pp. 1285–1290. doi: 10.1109/indin41052.2019.8972251.'
mla: Wunderlich, Paul, and Nemanja Hranisavljevic. “Comparison of Different Probabilistic
Graphical Models as Causal Models in Alarm Flood Reduction.” 2019 IEEE 17th
International Conference on Industrial Informatics (INDIN), edited by Aalto
University, Finland et al., IEEE, 2019, pp. 1285–90, https://doi.org/10.1109/indin41052.2019.8972251.
short: P. Wunderlich, N. Hranisavljevic, Comparison of Different Probabilistic Graphical
Models as Causal Models in Alarm Flood Reduction, IEEE, [Piscataway, NJ], 2019.
ufg: 'Wunderlich, Paul/Hranisavljevic, Nemanja: 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.'
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.'
conference:
end_date: 2019-07-25
location: 'Helsinki, Finland '
name: 17th IEEE International Conference on Industrial Informatics (INDIN)
start_date: 2019-07-22
corporate_editor:
- Aalto University, Finland
- Tampere University, Finland
- Finnish Society of Automation, Finland
date_created: 2025-04-16T06:29:04Z
date_updated: 2025-06-26T13:38:06Z
department:
- _id: DEP5023
doi: 10.1109/indin41052.2019.8972251
keyword:
- probabilistic graphical model
- causal model
- alarm flood reduction
- Bayesian network
- Markov chain
- restricted boltzmann machine
- automata
language:
- iso: eng
page: 1285-1290
place: '[Piscataway, NJ]'
publication: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
publication_identifier:
eisbn:
- 978-1-7281-2927-3
isbn:
- 978-1-7281-2928-0
publication_status: published
publisher: IEEE
status: public
title: Comparison of Different Probabilistic Graphical Models as Causal Models in
Alarm Flood Reduction
type: conference_editor_article
user_id: '83781'
year: '2019'
...
---
_id: '2088'
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."
author:
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
- first_name: Tim
full_name: Tack, Tim
last_name: Tack
citation:
ama: '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). Vol 6359. Lecture Notes in Computer Science.
Berlin: Springer; 2010:184-194. doi:https://doi.org/10.1007/978-3-642-16111-7_21'
apa: 'Niggemann, O., Lohweg, V., & Tack, T. (2010). A Probabilistic MajorClust
Variant for the Clustering of Near-Homogeneous Graphs. In 33rd Annual German
Conference on Artificial Intelligence (KI 2010) (Vol. 6359, pp. 184–194).
Berlin: Springer. https://doi.org/10.1007/978-3-642-16111-7_21'
bjps: 'Niggemann O, Lohweg V and Tack T (2010) A Probabilistic MajorClust
Variant for the Clustering of Near-Homogeneous Graphs. 33rd Annual German Conference
on Artificial Intelligence (KI 2010), vol. 6359. Berlin: Springer, pp. 184–194.'
chicago: 'Niggemann, Oliver, Volker Lohweg, and Tim Tack. “A Probabilistic MajorClust
Variant for the Clustering of Near-Homogeneous Graphs.” In 33rd Annual German
Conference on Artificial Intelligence (KI 2010), 6359:184–94. Lecture Notes
in Computer Science. Berlin: Springer, 2010. https://doi.org/10.1007/978-3-642-16111-7_21.'
chicago-de: 'Niggemann, Oliver, Volker Lohweg und Tim Tack. 2010. A Probabilistic
MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: 33rd
Annual German Conference on Artificial Intelligence (KI 2010), 6359:184–194.
Lecture Notes in Computer Science. Berlin: Springer. doi:https://doi.org/10.1007/978-3-642-16111-7_21,
.'
din1505-2-1: 'Niggemann, Oliver ;
Lohweg, Volker ; Tack,
Tim: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous
Graphs. In: 33rd Annual German Conference on Artificial Intelligence (KI 2010),
Lecture Notes in Computer Science. Bd. 6359. Berlin : Springer, 2010, S. 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.'
ieee: O. Niggemann, V. Lohweg, and T. Tack, “A Probabilistic MajorClust Variant
for the Clustering of Near-Homogeneous Graphs,” in 33rd Annual German Conference
on Artificial Intelligence (KI 2010), 2010, vol. 6359, pp. 184–194.
mla: Niggemann, Oliver, et al. “A Probabilistic MajorClust Variant for the Clustering
of Near-Homogeneous Graphs.” 33rd Annual German Conference on Artificial Intelligence
(KI 2010), vol. 6359, Springer, 2010, pp. 184–94, doi:https://doi.org/10.1007/978-3-642-16111-7_21.
short: 'O. Niggemann, V. Lohweg, T. Tack, in: 33rd Annual German Conference on Artificial
Intelligence (KI 2010), Springer, Berlin, 2010, pp. 184–194.'
ufg: 'Niggemann, Oliver et. al. (2010): A Probabilistic MajorClust Variant
for the Clustering of Near-Homogeneous Graphs, in: 33rd Annual German Conference
on Artificial Intelligence (KI 2010) (=Lecture Notes in Computer Science
6359), Berlin, S. 184–194.'
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).'
date_created: 2019-12-02T08:21:26Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
doi: https://doi.org/10.1007/978-3-642-16111-7_21
intvolume: ' 6359'
keyword:
- Markov Chain
- Cluster Algorithm
- Edge Weight
- Spectral Cluster
- Stable Distribution
language:
- iso: eng
main_file_link:
- url: https://link.springer.com/chapter/10.1007/978-3-642-16111-7_21
page: 184-194
place: Berlin
publication: 33rd Annual German Conference on Artificial Intelligence (KI 2010)
publication_identifier:
eisbn:
- 978-3-642-16111-7
isbn:
- 978-3-642-16110-0
publication_status: published
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs
type: conference
user_id: '45673'
volume: 6359
year: 2010
...