Publications¶
Summary¶
98 publications in total, including:
7 research monographs and textbooks (also open-access!)
See also: early drafts/preprints.
H-index = 20 (G**gle Scholar)
Featured Publications¶
Statistical Software¶
Gagolewski M., Deep R Programming, Melbourne, v1.0.0 edition, 2023, 456 pp., 🔓, DOI:10.5281/zenodo.7490464, URL:https://deepr.gagolewski.com/
Gagolewski M., Minimalist Data Wrangling with Python, Melbourne, v1.0.3 edition, 2023, 442 pp., 🔓, DOI:10.5281/zenodo.6451068, URL:https://datawranglingpy.gagolewski.com/
Gagolewski M., stringi: Fast and portable character string processing in R, Journal of Statistical Software 103(2), 1–59, 2022, DOI:10.18637/jss.v103.i02, URL:https://stringi.gagolewski.com/
Gagolewski M., genieclust: Fast and robust hierarchical clustering, SoftwareX 15, 100722, 2021, DOI:10.1016/j.softx.2021.100722, URL:https://genieclust.gagolewski.com/
Bartoszuk M., Gagolewski M., T-norms or t-conorms? How to aggregate similarity degrees for plagiarism detection, Knowledge-Based Systems 231, 107427, 2021, DOI:10.1016/j.knosys.2021.107427
Clustering¶
Gagolewski M., Normalised clustering accuracy: An asymmetric external cluster validity measure, Journal of Classification, 2024, in press, DOI:10.1007/s00357-024-09482-2
Gagolewski M., Cena A., Bartoszuk M., Brzozowski L., Clustering with minimum spanning trees: How good can it be?, Journal of Classification, 2024, in press, DOI:10.1007/s00357-024-09483-1
Gagolewski M., A framework for benchmarking clustering algorithms, SoftwareX 20, 101270, 2022, DOI:10.1016/j.softx.2022.101270, URL:https://clustering-benchmarks.gagolewski.com/
Gagolewski M., Bartoszuk M., Cena A., 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
Gagolewski M., Bartoszuk M., Cena A., 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/
Data Aggregation and Fusion¶
Pérez-Fernández R., De Baets B., Gagolewski M., 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
Gagolewski M., James S., Beliakov G., 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
Gagolewski M., Data Fusion: Theory, Methods, and Applications, Institute of Computer Science, Polish Academy of Sciences, Warsaw, 2015, 290 pp., 🔓, URL:https://github.com/gagolews/datafusion
Gagolewski M., 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
Mathematical Modelling and Applied Statistics¶
Bertoli-Barsotti L., Gagolewski M., Siudem G., Żogała-Siudem B., Gini-stable Lorenz curves and their relation to the generalised Pareto distribution, Journal of Informetrics 18(2), 101499, 2024, DOI:10.1016/j.joi.2024.101499
Siudem G., Nowak P., Gagolewski M., Power laws, the Price Model, and the Pareto type-2 distribution, Physica A: Statistical Mechanics and its Applications 606, 128059, 2022, DOI:10.1016/j.physa.2022.128059
Lasek J., Gagolewski M., Interpretable sports 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
Siudem G., Żogała-Siudem B., Cena A., Gagolewski M., 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
Lasek J., Gagolewski M., The efficacy of league formats in ranking teams, Statistical Modelling 18(5–6), 411–435, 2018, DOI:10.1177/1471082X18798426