Data Mining and Knowledge Discovery

, Volume 31, Issue 5, pp 1359–1390 | Cite as

MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data

  • Martin Becker
  • Florian Lemmerich
  • Philipp Singer
  • Markus Strohmaier
  • Andreas Hotho
Article
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2017

Abstract

Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.

Keywords

Heterogeneous sequence data Markov chain Model comparison Bayes factor Hyptrails Mixedtrails MTMC 

Notes

Acknowledgements

This work was partially funded by the BMBF project Kallimachos and the DFG German Science Fund research projects PoSTs II and p2map.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Data Mining and Information Retrieval GroupUniversity of WürzburgWürzburgGermany
  2. 2.GESIS - Leibniz Institute for the Social SciencesCologneGermany

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