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Journal Article

Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons188983

Boley,  Mario
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons104343

Goldsmith,  Bryan
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons21549

Ghiringhelli,  Luca M.
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons79525

Vreeken,  Jilles
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Fulltext (public)

s10618-017-0520-3.pdf
(Publisher version), 2MB

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Citation

Boley, M., Goldsmith, B., Ghiringhelli, L. M., & Vreeken, J. (2017). Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery. Data Mining and Knowledge Discovery, 31(5), 1391-1418. doi:10.1007/s10618-017-0520-3.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002D-99F7-B
Abstract
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective func- tions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the mean absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.