TY - GEN
T1 - Comparing Hypotheses About Sequential Data
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
AU - Lemmerich, Florian
AU - Singer, Philipp
AU - Becker, Martin
AU - Espin-Noboa, Lisette
AU - Dimitrov, Dimitar
AU - Helic, Denis
AU - Hotho, Andreas
AU - Strohmaier, Markus
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85040258441&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-71273-4_30
DO - 10.1007/978-3-319-71273-4_30
M3 - Conference paper
AN - SCOPUS:85040258441
SN - 9783319712727
T3 - Lecture Notes in Computer Science
SP - 354
EP - 357
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
PB - Springer Verlag Wien
CY - Cham
Y2 - 18 September 2017 through 22 September 2017
ER -