Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications

Florian Lemmerich; Philipp Singer; Martin Becker; Lisette Espin-Noboa; Dimitar Dimitrov; Denis Helic; Andreas Hotho; Markus Strohmaier

doi:10.1007/978-3-319-71273-4_30

Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications

Florian Lemmerich, Philipp Singer, Martin Becker, Lisette Espin-Noboa, Dimitar Dimitrov, Denis Helic, Andreas Hotho, Markus Strohmaier

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Sequential data can be found in many settings, e.g., as sequences of visited websites or as location sequences of travellers. To improve the understanding of the underlying mechanisms that generate such sequences, the HypTrails approach provides for a novel data analysis method. Based on first-order Markov chain models and Bayesian hypothesis testing, it allows for comparing a set of hypotheses, i.e., beliefs about transitions between states, with respect to their plausibility considering observed data. HypTrails has been successfully employed to study phenomena in the online and the offline world. In this talk, we want to give an introduction to HypTrails and showcase selected real-world applications on urban mobility and reading behavior on Wikipedia.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
Publisher	Springer Verlag Wien
Pages	354-357
Number of pages	4
ISBN (Print)	9783319712727
DOIs	https://doi.org/10.1007/978-3-319-71273-4_30
Publication status	Published - 2017
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Skopje, Macedonia, The Former Yugoslav Republic of Duration: 18 Sept 2017 → 22 Sept 2017

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag Wien

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated title	ECML PKDD 2017
Country/Territory	Macedonia, The Former Yugoslav Republic of
City	Skopje
Period	18/09/2017 → 22/09/2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Access to Document

10.1007/978-3-319-71273-4_30

Cite this

Lemmerich, F., Singer, P., Becker, M., Espin-Noboa, L., Dimitrov, D., Helic, D., Hotho, A., & Strohmaier, M. (2017). Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings (pp. 354-357). (Lecture Notes in Computer Science). Springer Verlag Wien. https://doi.org/10.1007/978-3-319-71273-4_30

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author = "Florian Lemmerich and Philipp Singer and Martin Becker and Lisette Espin-Noboa and Dimitar Dimitrov and Denis Helic and Andreas Hotho and Markus Strohmaier",

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Lemmerich, F, Singer, P, Becker, M, Espin-Noboa, L, Dimitrov, D, Helic, D, Hotho, A & Strohmaier, M 2017, Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Lecture Notes in Computer Science, Springer Verlag Wien, pp. 354-357, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Skopje, Macedonia, The Former Yugoslav Republic of, 18/09/2017. https://doi.org/10.1007/978-3-319-71273-4_30

Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications. / Lemmerich, Florian; Singer, Philipp; Becker, Martin et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Springer Verlag Wien, 2017. p. 354-357 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Helic, Denis

AU - Hotho, Andreas

AU - Strohmaier, Markus

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Lemmerich F, Singer P, Becker M, Espin-Noboa L, Dimitrov D, Helic D et al. Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Springer Verlag Wien. 2017. p. 354-357. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-71273-4_30

Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications

Abstract

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UN SDGs

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