Publications
Summary
86 publications in total, including:
42 journal papers,
34 papers in conference proceedings,
7 research monographs and textbooks,
3 edited volumes.
My ORCID == 0000-0003-0637-6028
Current h-index == 16 (Google Scholar) / 12 (Scopus) / 11 (Web of Science).
Featured Papers
M. Gagolewski, stringi: Fast and portable character string processing in R, Journal of Statistical Software, 2022, in press, url:https://stringi.gagolewski.com
M. Gagolewski, M. Bartoszuk, A. Cena, Are cluster validity measures (in)valid?, Information Sciences 581, 620–636, 2021, doi:10.1016/j.ins.2021.10.004, url:https://github.com/gagolews/optim_cvi
J. Lasek, M. Gagolewski, Interpretable sport team rating models based on the gradient descent algorithm, International Journal of Forecasting 37(3), 1061–1071, 2021, doi:10.1016/j.ijforecast.2020.11.008
G. Siudem, B. Żogała-Siudem, A. Cena, M. Gagolewski, Three dimensions of scientific impact, Proceedings of the National Academy of Sciences of the United States of America (PNAS) 117, 13896–13900, 2020, doi:10.1073/pnas.2001064117
R. Pérez-Fernández, B. De Baets, M. Gagolewski, A taxonomy of monotonicity properties for the aggregation of multidimensional data, Information Fusion 52, 322–334, 2019, doi:10.1016/j.inffus.2019.05.006
M. Gagolewski, S. James, G. Beliakov, Supervised learning to aggregate data with the Sugeno integral, IEEE Transactions on Fuzzy Systems 27(4), 810–815, 2019, doi:10.1109/TFUZZ.2019.2895565
M. Gagolewski, M. Bartoszuk, A. Cena, Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm, Information Sciences 363, 8–23, 2016, doi:10.1016/j.ins.2016.05.003, url:https://genieclust.gagolewski.com
M. Gagolewski, Spread measures and their relation to aggregation functions, European Journal of Operational Research 241(2), 469–477, 2015, doi:10.1016/j.ejor.2014.08.034
M. Gagolewski, Statistical hypothesis test for the difference between Hirsch indices of two Pareto-distributed random samples, in: R. Kruse et al. (eds.), Synergies of Soft Computing and Statistics for Intelligent Data Analysis, Springer, 2013, pp. 359–367, doi:10.1007/978-3-642-33042-1_39