nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Towards a General Method for Logical Rule Extraction from Time Series

verfasst von : Guido Sciavicco, Ionel Eduard Stan, Alessandro Vaccari

Erschienen in: From Bioinspired Systems and Biomedical Applications to Machine Learning

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Extracting rules from temporal series is a well-established temporal data mining technique. The current literature contains a number of different algorithms and experiments that allow one to abstract temporal series and, later, extract meaningful rules from them. In this paper, we approach this problem in a rather general way, without resorting, as many other methods, to expert knowledge and ad-hoc solutions. Our very simple temporal abstraction method allows us to transform time series into timelines, which can be then used for logical temporal rule extraction using an already existing temporal adaptation of the algorithm APRIORI. We have tested this approach on real data, obtaining promising results.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nächstes Kapitel A Principled Two-Step Method for Example-Dependent Cost Binary Classification

Agrawal, R., Imieliński, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)

Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th International Conference on Data Engineering, pp. 3–14 (1995)

Bresolin, D., Cominato, E., Gnani, S., Muñoz-Velasco, E., Sciavicco, G.: Extracting interval temporal logic rules: a first approach. In: Proceedings of the 25th International Symposium on Temporal Representation and Reasoning. Leibniz International Proceedings in Informatics, vol. 120, pp. 7:1–7:15 (2018)

Bresolin, D., Kurucz, A., Muñoz-Velasco, E., Ryzhikov, V., Sciavicco, G., Zakharyaschev, M.: Horn fragments of the Halpern-Shoham interval temporal logic. ACM Trans. Comput. Logic 18(3), 22:1–22:39 (2017)MathSciNetCrossRef

Bresolin, D., Muñoz-Velasco, E., Sciavicco, G.: Sub-propositional fragments of the interval temporal logic of Allen’s relations. In: Fermé, E., Leite, J. (eds.) JELIA 2014. LNCS (LNAI), vol. 8761, pp. 122–136. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11558-0_9CrossRef

Cotofrei, P., Stoffel, K.: Rule extraction from time series databases using classification trees. In: Proceedings of the IASTED International Conference on Applied Informatics, pp. 327–332 (2002)

Das, G., Lin, K., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 16–22 (1998)

Fürnkranz, J., Gamberger, D., Lavrac, N.: Foundations of Rule Learning. Cognitive Technologies. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-540-75197-7CrossRefMATH

Fürnkranz, J., Kliegr, T.: A brief overview of rule learning. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 54–69. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21542-6_4CrossRef

10.

Halpern, J., Shoham, Y.: A propositional modal logic of time intervals. J. ACM 38(4), 935–962 (1991)MathSciNetCrossRef

11.

Höppner, F., Peter, S.: Temporal interval pattern languages to characterize time flow. Wiley Interdisc. Rev. Data Mining Knowl. Discov. 4(3), 196–212 (2014)CrossRef

12.

Höppner, F.: Time series abstraction methods - a survey. In: Proceedings of the 32nd Annual Meeting of the Society for Informatics, pp. 777–786 (2002)

13.

Jakkula, V., Cook, D., Jain, G.: Prediction models for a smart home based health care system. In: Proceedings of the 21st International Conference on Advanced Information Networking and Applications, pp. 761–765 (2007)

14.

Kurbalija, V., et al.: Time-series analysis in the medical domain: a study of tacrolimus administration and influence on kidney graft function. Comput. Biol. Med. 50, 19–31 (2014)CrossRef

15.

Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Mining Knowl. Discov. 15(2), 107–144 (2007)MathSciNetCrossRef

16.

Mörchen, F., Ultsch, A.: Optimizing time series discretization for knowledge discovery. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 660–665. ACM (2005)

17.

Moskovitch, R., Shahar, Y.: Classification-driven temporal discretization of multivariate time series. Data Mining Knowl. Discov. 29(4), 871–913 (2015)MathSciNetCrossRef

18.

Muggleton, S.: Inductive logic programming: issues, results and the challenge of learning language in logic. Artif. Intell. 114(1–2), 283–296 (1999)CrossRef

19.

Raedt, L.D.: Logical and Relational Learning. Cognitive Technologies. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68856-3CrossRefMATH

20.

Sacchi, L., Larizza, C., Combi, C., Bellazzi, R.: Data mining with temporal abstractions: learning rules from time series. Data Mining Knowl. Discov. 15(2), 217–247 (2007)MathSciNetCrossRef

21.

Sheremetov, L., González-Sánchez, A., López-Yáñez, I., Ponomarev, A.: Time series forecasting: applications to the upstream oil and gas supply chain. In: Proceedings of the 7th IFAC Conference on Manufacturing Modelling, Management, and Control. IFAC Proc. Vol. 46, 957–962 (2013)CrossRef

22.

Vito, S.D., Piga, M., Martinotto, L., Francia, G.D.: CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic Bayesian regularization. Sens. Actuators B: Chem. 143, 182–191 (2009)CrossRef

Titel: Towards a General Method for Logical Rule Extraction from Time Series
verfasst von: Guido Sciavicco
Ionel Eduard Stan
Alessandro Vaccari
Verlag: Springer International Publishing
Buch: From Bioinspired Systems and Biomedical Applications to Machine Learning
Print ISBN: 978-3-030-19650-9

Electronic ISBN: 978-3-030-19651-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-19651-6_1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"