Abstract
The purpose of pattern mining is to help experts understand their data. Following the assumption that an analyst expects neighbouring patterns to show similar behavior, we investigate the interestingness of a pattern given its neighborhood. We define a new way of selecting outstanding patterns, based on an order relation between patterns and a quality score. An outstanding pattern shows only small syntactic variations compared to its neighbors but deviates strongly in quality. Using several supervised quality measures, we show experimentally that only very few patterns turn out to be outstanding. We also illustrate our approach with patterns mined from molecular data.
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Notes
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We normalize the growth rate because the unnormalized growth rate can have \(\infty \) as a value, which prevents the calculation of mean and standard deviation.
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We direct the interested reader to the original publication.
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This work was partially funded by the ANR project InvolvD (ANR-20-CE23-0023).
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Lehembre, E. et al. (2022). Selecting Outstanding Patterns Based on Their Neighbourhood. In: Bouadi, T., Fromont, E., Hüllermeier, E. (eds) Advances in Intelligent Data Analysis XX. IDA 2022. Lecture Notes in Computer Science, vol 13205. Springer, Cham. https://doi.org/10.1007/978-3-031-01333-1_15
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