Publication List (by Type)

This list is also available in BibTeX format. Some preprints are available here.

Journal Articles (39)

  1. M. Gagolewski, M. Bartoszuk, A. Cena, Are cluster validity measures (in)valid?, Information Sciences, 2021, in press, doi:10.1016/j.ins.2021.10.004, url:https://github.com/gagolews/optim_cvi

  2. M. Gagolewski, stringi: Fast and portable character string processing in R, Journal of Statistical Software, 2021, in press, url:https://stringi.gagolewski.com

  3. M. Bartoszuk, M. Gagolewski, 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

  4. G. Beliakov, M. Gagolewski, S. James, Hierarchical data fusion processes involving the Möbius representation of capacities, Fuzzy Sets and Systems, 2021, in press, doi:10.1016/j.fss.2021.02.006

  5. 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

  6. M. Gagolewski, genieclust: Fast and robust hierarchical clustering, SoftwareX 15, 100722, 2021, doi:10.1016/j.softx.2021.100722, url:https://genieclust.gagolewski.com

  7. R. Pérez-Fernández, M. Gagolewski, B. De Baets, On the aggregation of compositional data, Information Fusion 73, 103–110, 2021, doi:10.1016/j.inffus.2021.02.021

  8. G. Beliakov, M. Gagolewski, S. James, DC optimization for constructing discrete Sugeno integrals and learning nonadditive measures, Optimization 69(12), 2515–2534, 2020, doi:10.1080/02331934.2019.1705300

  9. M. Bartoszuk, M. Gagolewski, SimilaR: R Code Clone and Plagiarism Detection, R Journal 12(1), 367–385, 2020, doi:10.32614/RJ-2020-017, url:https://CRAN.R-project.org/package=SimilaR

  10. 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

  11. L. Coroianu, R. Fullér, M. Gagolewski, S. James, Constrained ordered weighted averaging aggregation with multiple comonotone constraints, Fuzzy Sets and Systems 395, 21–39, 2020, doi:10.1016/j.fss.2019.09.006

  12. A. Cena, M. Gagolewski, Genie+OWA: Robustifying hierarchical clustering with OWA-based linkages, Information Sciences 520, 324–336, 2020, doi:10.1016/j.ins.2020.02.025

  13. M. Gagolewski, R. Pérez-Fernández, B. De Baets, An inherent difficulty in the aggregation of multidimensional data, IEEE Transactions on Fuzzy Systems 28, 602–606, 2020, doi:10.1109/TFUZZ.2019.2908135

  14. G. Beliakov, M. Gagolewski, S. James, Robust fitting for the Sugeno integral with respect to general fuzzy measures, Information Sciences 514, 449–461, 2020, doi:10.1016/j.ins.2019.11.024

  15. A. Geras, G. Siudem, M. Gagolewski, Should we introduce a dislike button for academic papers?, Journal of the Association for Information Science and Technology 71(2), 221–229, 2020, doi:10.1002/ASI.24231

  16. L. Coroianu, M. Gagolewski, P. Grzegorzewski, Piecewise linear approximation of fuzzy numbers: Algorithms, arithmetic operations and stability of characteristics, Soft Computing 23(19), 9491–9505, 2019, doi:10.1007/s00500-019-03800-2, url:https://CRAN.R-project.org/package=FuzzyNumbers

  17. G. Beliakov, M. Gagolewski, S. James, Aggregation on ordinal scales with the Sugeno integral for biomedical applications, Information Sciences 501, 377–387, 2019, doi:10.1016/j.ins.2019.06.023

  18. 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

  19. 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

  20. G. Beliakov, M. Gagolewski, S. James, S. Pace, N. Pastorello, E. Thilliez, R. Vasa, Measuring traffic congestion: An approach based on learning weighted inequality, spread and aggregation indices from comparison data, Applied Soft Computing 67, 910–919, 2019, doi:10.1016/j.asoc.2017.07.014

  21. J. Lasek, M. Gagolewski, The efficacy of league formats in ranking teams, Statistical Modelling 18(5–6), 411–435, 2018, doi:10.1177/1471082X18798426

  22. M. Gagolewski, Penalty-based aggregation of multidimensional data, Fuzzy Sets and Systems 325, 4–20, 2017, doi:10.1016/j.fss.2016.12.009

  23. R. Mesiar, M. Gagolewski, H-index and other Sugeno integrals: Some defects and their compensation, IEEE Transactions on Fuzzy Systems 24(6), 1668–1672, 2016, doi:10.1109/TFUZZ.2016.2516579

  24. G. Beliakov, M. Gagolewski, S. James, Penalty-based and other representations of economic inequality, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24(Suppl.1), 1–23, 2016, doi:10.1142/S0218488516400018

  25. 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

  26. J. Lasek, Z. Szlavik, M. Gagolewski, S. Bhulai, How to improve a team’s position in the FIFA ranking – A simulation study, Journal of Applied Statistics 43(7), 1349–1368, 2016, doi:10.1080/02664763.2015.1100593

  27. B. Żogała-Siudem, G. Siudem, A. Cena, M. Gagolewski, Agent-based model for the bibliometric h-index – Exact solution, European Physical Journal B 89(21), 2016, doi:10.1140/epjb/e2015-60757-1

  28. A. Cena, M. Gagolewski, R. Mesiar, Problems and challenges of information resources producers’ clustering, Journal of Informetrics 9(2), 2015, doi:10.1016/j.joi.2015.02.005

  29. A. Cena, M. Gagolewski, OM3: Ordered maxitive, minitive, and modular aggregation operators – Axiomatic and probabilistic properties in an arity-monotonic setting, Fuzzy Sets and Systems 264, 138–159, 2015, doi:10.1016/j.fss.2014.04.001

  30. 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

  31. M. Gagolewski, R. Mesiar, Monotone measures and universal integrals in a uniform framework for the scientific impact assessment problem, Information Sciences 263, 166–174, 2014, doi:10.1016/j.ins.2013.12.004

  32. M. Gagolewski, On the relationship between symmetric maxitive, minitive, and modular aggregation operators, Information Sciences 221, 170–180, 2013, doi:10.1016/j.ins.2012.09.005

  33. L. Coroianu, M. Gagolewski, P. Grzegorzewski, Nearest piecewise linear approximation of fuzzy numbers, Fuzzy Sets and Systems 233, 26–51, 2013, doi:10.1016/j.fss.2013.02.005, url:https://CRAN.R-project.org/package=FuzzyNumbers

  34. M. Gagolewski, Scientific impact assessment cannot be fair, Journal of Informetrics 7(4), 792–802, 2013, doi:10.1016/j.joi.2013.07.001

  35. M. Gagolewski, R. Mesiar, Aggregating different paper quality measures with a generalized h-index, Journal of Informetrics 6(4), 566–579, 2012, doi:10.1016/j.joi.2012.05.001

  36. M. Gagolewski, Bibliometric impact assessment with R and the CITAN package, Journal of Informetrics 5(4), 678–692, 2011, doi:10.1016/j.joi.2011.06.006, url:https://CRAN.R-project.org/package=CITAN

  37. M. Gagolewski, P. Grzegorzewski, Possibilistic analysis of arity-monotonic aggregation operators and its relation to bibliometric impact assessment of individuals, International Journal of Approximate Reasoning 52(9), 1312–1324, 2011, doi:10.1016/j.ijar.2011.01.010

  38. M. Gagolewski, P. Grzegorzewski, A geometric approach to the construction of scientific impact indices, Scientometrics 81(3), 617–634, 2009, doi:10.1007/s11192-008-2253-y

  39. T. Rowiński, M. Gagolewski, Preferencje i postawy wobec pomocy online (Attitudes towards online counselling and psychotherapy), Studia Psychologica UKSW 7, 195–210, 2007, in Polish

Papers in Conference Proceedings (34)

  1. L. Coroianu, M. Gagolewski, Penalty-based data aggregation in real normed vector spaces, in: R. Halaš et al. (eds.), New Trends in Aggregation Theory, Springer, 2019, pp. 160–171, doi:10.1007/978-3-030-19494-9_15

  2. G. Beliakov, M. Gagolewski, S. James, Least median of squares (LMS) and least trimmed squares (LTS) fitting for the weighted arithmetic mean, in: J. Medina et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations, Springer, 2018, pp. 367–378, doi:10.1007/978-3-319-91476-3_31

  3. M. Gagolewski, S. James, Fitting symmetric fuzzy measures for discrete Sugeno integration, in: J. Kacprzyk et al. (eds.), Advances in Fuzzy Logic and Technology 2017, Springer, 2018, pp. 104–116, doi:10.1007/978-3-319-66824-6_10

  4. M. Bartoszuk, M. Gagolewski, Binary aggregation functions in software plagiarism detection, in: Proc. FUZZ-IEEE’17, IEEE, 2017, no. 8015582, doi:10.1109/FUZZ-IEEE.2017.8015582

  5. A. Cena, M. Gagolewski, OWA-based linkage and the Genie correction for hierarchical clustering, in: Proc. FUZZ-IEEE’17, IEEE, 2017, no. 8015652, doi:10.1109/FUZZ-IEEE.2017.8015652

  6. M. Gagolewski, A. Cena, M. Bartoszuk, Hierarchical clustering via penalty-based aggregation and the Genie approach, in: V. Torra et al. (eds.), Modeling Decisions for Artificial Intelligence, Springer, 2016, pp. 191–202, doi:10.1007/978-3-319-45656-0_16

  7. A. Cena, M. Gagolewski, Fuzzy k-minpen clustering and k-nearest-minpen classification procedures incorporating generic distance-based penalty minimizers, in: J. Carvalho et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Springer, 2016, pp. 445–456, doi:10.1007/978-3-319-40581-0_36

  8. M. Bartoszuk, G. Beliakov, M. Gagolewski, S. James, Fitting aggregation functions to data: Part I – Linearization and regularization, in: J. Carvalho et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Springer, 2016, pp. 767–779, doi:10.1007/978-3-319-40581-0_62

  9. M. Bartoszuk, G. Beliakov, M. Gagolewski, S. James, Fitting aggregation functions to data: Part II – Idempotization, in: J. Carvalho et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Springer, 2016, pp. 780–789, doi:10.1007/978-3-319-40581-0_63

  10. M. Bartoszuk, M. Gagolewski, Detecting similarity of R functions via a fusion of multiple heuristic methods, in: J. Alonso, H. Bustince, M. Reformat (eds.), Proc. IFSA/EUSFLAT’15, Atlantis Press, 2015, pp. 419–426, doi:10.2991/ifsa-eusflat-15.2015.61

  11. A. Cena, M. Gagolewski, A K-means-like algorithm for informetric data clustering, in: J. Alonso, H. Bustince, M. Reformat (eds.), Proc. IFSA/EUSFLAT’15, Atlantis Press, 2015, pp. 536–543, doi:10.2991/ifsa-eusflat-15.2015.77

  12. M. Gagolewski, Sugeno integral-based confidence intervals for the theoretical h-index, in: P. Grzegorzewski et al. (eds.), Strengthening Links Between Data Analysis and Soft Computing, Springer, 2015, pp. 233–240, doi:10.1007/978-3-319-10765-3_28

  13. M. Gagolewski, Normalized WD\(_p\)WAM and WD\(_p\)OWA spread measures, in: J. Alonso, H. Bustince, M. Reformat (eds.), Proc. IFSA/EUSFLAT’15, Atlantis Press, 2015, pp. 210–216, doi:10.2991/ifsa-eusflat-15.2015.32

  14. M. Gagolewski, J. Lasek, The use of fuzzy relations in the assessment of information resources producers’ performance, in: Proc. 7th IEEE International Conference Intelligent Systems IS\rq2014, Vol. 2: Tools, Architectures, Systems, Applications, Springer, 2015, pp. 289–300, doi:10.1007/978-3-319-11310-4_25

  15. M. Gagolewski, J. Lasek, Learning experts’ preferences from informetric data, in: J. Alonso, H. Bustince, M. Reformat (eds.), Proc. IFSA/EUSFLAT’15, Atlantis Press, 2015, pp. 484–491, doi:10.2991/ifsa-eusflat-15.2015.70

  16. J. Lasek, M. Gagolewski, Estimation of tournament metrics for association football league formats, in: Selected problems in information technologies (Proc. ITRIA’15 vol. 2), Institute of Computer Science, Polish Academy of Sciences, 2015, pp. 67–78

  17. J. Lasek, M. Gagolewski, The winning solution to the AAIA’15 Data Mining Competition: Tagging firefighter activities at a fire scene, in: M. Ganzha, L. Maciaszek, M. Paprzycki (eds.), Proc. FedCSIS’15, IEEE, 2015, pp. 375–380, doi:10.15439/2015F418

  18. A. Cena, M. Gagolewski, Clustering and aggregation of informetric data sets, in: Computational methods in data analysis (Proc. ITRIA’15 vol. 1), Institute of Computer Science, Polish Academy of Sciences, 2015, pp. 5–26

  19. M. Gagolewski, Some issues in aggregation of multidimensional data, in: M. Baczyński, B. De Baets, R. Mesiar (eds.), Proc. 8th International Summer School on Aggregation Operators (AGOP 2015), University of Silesia, Katowice, Poland, 2015, pp. 127–132

  20. A. Cena, M. Gagolewski, Aggregation and soft clustering of informetric data, in: M. Baczyński, B. De Baets, R. Mesiar (eds.), Proc. 8th International Summer School on Aggregation Operators (AGOP 2015), University of Silesia, Katowice, Poland, 2015, pp. 79–84

  21. L. Coroianu, M. Gagolewski, P. Grzegorzewski, M. Adabitabar Firozja, T. Houlari, Piecewise linear approximation of fuzzy numbers preserving the support and core, in: A. Laurent et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Springer, 2014, pp. 244–254, doi:10.1007/978-3-319-08855-6_25

  22. M. Bartoszuk, M. Gagolewski, A fuzzy R code similarity detection algorithm, in: A. Laurent et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part III, Springer, 2014, pp. 21–30, doi:10.1007/978-3-319-08852-5_3

  23. M. Gagolewski, M. Dębski, M. Nowakiewicz, Efficient algorithm for computing certain graph-based monotone integrals: The \(l_p\)-indices, in: R. Mesiar, T. Bacigal (eds.), Proc. Uncertainty Modeling, STU Bratislava, 2013, pp. 17–23

  24. A. Cena, M. Gagolewski, OM3: Ordered maxitive, minitive, and modular aggregation operators – Part I: Axiomatic analysis under arity-dependence, in: H. Bustince et al. (eds.), Aggregation Functions in Theory and in Practise, Springer, 2013, pp. 93–103, doi:10.1007/978-3-642-39165-1_13

  25. A. Cena, M. Gagolewski, OM3: Ordered maxitive, minitive, and modular aggregation operators – Part II: A simulation study, in: H. Bustince et al. (eds.), Aggregation Functions in Theory and in Practise, Springer, 2013, pp. 105–115, doi:10.1007/978-3-642-39165-1_14

  26. 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

  27. M. Gagolewski, On the relation between effort-dominating and symmetric minitive aggregation operators, in: S. Greco et al. (eds.), Advances in Computational Intelligence, Part III, Springer, 2012, pp. 276–285, doi:10.1007/978-3-642-31718-7_29

  28. M. Gagolewski, P. Grzegorzewski, Axiomatic characterizations of (quasi-) L-statistics and S-statistics and the Producer Assessment Problem, in: S. Galichet et al. (eds.), Proc. EUSFLAT/LFA’11, Atlantis Press, 2011, pp. 53–58, doi:10.2991/eusflat.2011.112

  29. T. Rowiński, M. Gagolewski, Internet a kryzys, in: M. Jankowska, M. Starzomska (eds.), Kryzys: Pułapka czy szansa?, WN Akapit, Warsaw, Poland, 2011, pp. 211–224, in Polish

  30. M. Gagolewski, P. Grzegorzewski, Arity-monotonic extended aggregation operators, in: E. H”ullermeier et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer, 2010, pp. 693–702, doi:10.1007/978-3-642-14055-6_73

  31. M. Gagolewski, P. Grzegorzewski, S-statistics and their basic properties, in: C. Borgelt et al. (eds.), Combining Soft Computing and Statistical Methods in Data Analysis, Springer, 2010, pp. 281–288, doi:10.1007/978-3-642-14746-3_35

  32. M. Gagolewski, P. Grzegorzewski, Metody i problemy naukometrii (Methods and problems of scientometrics), in: T. Rowiński, R. Tadeusiewicz (eds.), Psychologia i informatyka. Synergia i kontradykcje, Wyd. UKSW, Warsaw, Poland, 2010, pp. 103–125, in Polish

  33. M. Gagolewski, P. Grzegorzewski, O pewnym uogólnieniu indeksu Hirscha, in: P. Kawalec, P. Lipski (eds.), Kadry i infrastruktura nowoczesnej nauki: Teoria i praktyka, Proc. 1st Intl. Conf. Zarządzanie Nauką, Wydawnictwo Lubelskiej Szkoły Biznesu, Lublin, 2009, pp. 15–29, in Polish

  34. M. Gagolewski, P. Grzegorzewski, Possible and necessary h-indices, in: J. Carvalho et al. (eds.), Proc. IFSA/EUSFLAT’09, IFSA, 2009, pp. 1691–1695

Research Monographs and Textbooks (6)

  1. M. Gagolewski, Lightweight Machine Learning Classics with R, e-book, 2021, draft v0.2.1, doi:10.5281/zenodo.4539689, url:https://lmlcr.gagolewski.com/

  2. M. Gagolewski, M. Bartoszuk, A. Cena, Przetwarzanie i analiza danych w języku Python (Data Processing and Analysis in Python), Wydawnictwo Naukowe PWN, Warsaw, Poland, 2016, 369 pp., in Polish, url:https://github.com/gagolews/Analiza_danych_w_jezyku_Python

  3. M. Gagolewski, Programowanie w języku R. Analiza danych, obliczenia, symulacje (R Programming. Data Analysis, Computing, Simulations), Wydawnictwo Naukowe PWN, Warsaw, Poland, 2nd edition, 2016, 550 pp., in Polish, url:https://github.com/gagolews/Programowanie_w_jezyku_R/

  4. M. Gagolewski, Algorytmy i postawy programowania w języku C++ (Introduction to Algorithms and Programming in C++), e-book, Warsaw, Poland, 2016, 176 pp., in Polish, url:https://github.com/gagolews/aipp

  5. M. Gagolewski, Data Fusion: Theory, Methods, and Applications, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, 2015, 290 pp., url:https://raw.githubusercontent.com/gagolews/bibliography/master/preprints/2015datafusionbook.pdf

  6. P. Grzegorzewski, M. Gagolewski, K. Bobecka-Wesołowska, Wnioskowanie statystyczne z wykorzystaniem środowiska R (Statistical Inference with R), Politechnika Warszawska, Warsaw, Poland, 2014, 183 pp., in Polish, url:https://raw.githubusercontent.com/gagolews/bibliography/master/preprints/2014wnioskowaniestatystyczne.pdf

Edited Volumes (3)

  1. R. Halaš, M. Gagolewski, R. Mesiar (eds.), New Trends in Aggregation Theory, Springer, 2019, 348 pp., doi:10.1007/978-3-030-19494-9

  2. M. Ferraro, P. Giordani, B. Vantaggi, M. Gagolewski, M. Gil, P. Grzegorzewski, O. Hryniewicz (eds.), Soft Methods for Data Science, Springer, 2017, 535 pp., doi:10.1007/978-3-319-42972-4

  3. P. Grzegorzewski, M. Gagolewski, O. Hryniewicz, M. Gil (eds.), Strengthening Links Between Data Analysis and Soft Computing, Springer, 2015, 294 pp., doi:10.1007/978-3-319-10765-3