Abstract
Many data exploration tasks require a target class. Unfortunately, the data is not always labeled with respect to this desired class. Rather than using unsupervised methods or a labeling pre-processing, this paper proposes an interactive system that discovers this target class and characterizes it at the same time. More precisely, we introduce a new interactive pattern mining method that learns which part of the dataset is really interesting for the user. By integrating user feedback about patterns, our method aims at sampling patterns with a probability proportional to their frequency in the interesting transactions. We demonstrate that it accurately identifies the target class if user feedback is consistent. Experiments also show this method has a good true and false positive rate enabling to present relevant patterns to the user.
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Notes
- 1.
It is also possible to set weights to 0 or 1 if the labels of some transactions are already known.
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Acknowledgements
This work has been partially supported by the Decade project, Mastodons 2017, CNRS.
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Giacometti, A., Soulet, A. (2017). Interactive Pattern Sampling for Characterizing Unlabeled Data. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham. https://doi.org/10.1007/978-3-319-68765-0_9
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