%0 Conference Paper %1 lemmerich2016mining %A Lemmerich, Florian %A Becker, Martin %A Singer, Philipp %A Helic, Denis %A Hotho, Andreas %A Strohmaier, Markus %B Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining %C New York, NY, USA %D 2016 %I ACM %K bayes behavior chain cv discovery diss diss:allmypubs emm exceptional factor human hyptrails inthesis kdd markov mining model myown navigation p21 project:2023-tracking selected subgroup subgroups transition %P 965--974 %R 10.1145/2939672.2939752 %T Mining Subgroups with Exceptional Transition Behavior %U http://doi.acm.org/10.1145/2939672.2939752 %X We present a new method for detecting interpretable subgroups with exceptional transition behavior in sequential data. Identifying such patterns has many potential applications, e.g., for studying human mobility or analyzing the behavior of internet users. To tackle this task, we employ exceptional model mining, which is a general approach for identifying interpretable data subsets that exhibit unusual interactions between a set of target attributes with respect to a certain model class. Although exceptional model mining provides a well-suited framework for our problem, previously investigated model classes cannot capture transition behavior. To that end, we introduce first-order Markov chains as a novel model class for exceptional model mining and present a new interestingness measure that quantifies the exceptionality of transition subgroups. The measure compares the distance between the Markov transition matrix of a subgroup and the respective matrix of the entire data with the distance of random dataset samples. In addition, our method can be adapted to find subgroups that match or contradict given transition hypotheses. We demonstrate that our method is consistently able to recover subgroups with exceptional transition models from synthetic data and illustrate its potential in two application examples. Our work is relevant for researchers and practitioners interested in detecting exceptional transition behavior in sequential data. %@ 978-1-4503-4232-2

@inproceedings{lemmerich2016mining, abstract = {We present a new method for detecting interpretable subgroups with exceptional transition behavior in sequential data. Identifying such patterns has many potential applications, e.g., for studying human mobility or analyzing the behavior of internet users. To tackle this task, we employ exceptional model mining, which is a general approach for identifying interpretable data subsets that exhibit unusual interactions between a set of target attributes with respect to a certain model class. Although exceptional model mining provides a well-suited framework for our problem, previously investigated model classes cannot capture transition behavior. To that end, we introduce first-order Markov chains as a novel model class for exceptional model mining and present a new interestingness measure that quantifies the exceptionality of transition subgroups. The measure compares the distance between the Markov transition matrix of a subgroup and the respective matrix of the entire data with the distance of random dataset samples. In addition, our method can be adapted to find subgroups that match or contradict given transition hypotheses. We demonstrate that our method is consistently able to recover subgroups with exceptional transition models from synthetic data and illustrate its potential in two application examples. Our work is relevant for researchers and practitioners interested in detecting exceptional transition behavior in sequential data.}, acmid = {2939752}, added-at = {2017-11-14T18:40:18.000+0100}, address = {New York, NY, USA}, author = {Lemmerich, Florian and Becker, Martin and Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, biburl = {https://www.bibsonomy.org/bibtex/2da1789fa70d47d2fc70031643e56a860/becker}, booktitle = {Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, description = {impactfactor = {5.51}, impactfactor-year = 2016, impactfactor-source = {https://www.scimagojr.com/journalsearch.php?q=101510&tip=sid}, impactfactor-notes = {A bit questionable if this is a valid impact factor.}}, doi = {10.1145/2939672.2939752}, interhash = {fa199ed068a057ac9dedf5a53d1dff3a}, intrahash = {da1789fa70d47d2fc70031643e56a860}, isbn = {978-1-4503-4232-2}, keywords = {bayes behavior chain cv discovery diss diss:allmypubs emm exceptional factor human hyptrails inthesis kdd markov mining model myown navigation p21 project:2023-tracking selected subgroup subgroups transition}, location = {San Francisco, California, USA}, numpages = {10}, pages = {965--974}, publisher = {ACM}, series = {KDD '16}, timestamp = {2023-12-15T12:08:49.000+0100}, title = {Mining Subgroups with Exceptional Transition Behavior}, url = {http://doi.acm.org/10.1145/2939672.2939752}, year = 2016 }