Publication List (by Year)

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

  1. M. Gagolewski, Minimalist Data Wrangling with Python, open-access textbook, 2022, v0.4.1, doi:10.5281/zenodo.6451068, url:https://datawranglingpy.gagolewski.com/

  2. M. Gagolewski, 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

  3. G. Beliakov, M. Gagolewski, S. James, Reduction of variables and constraints in fitting antibuoyant fuzzy measures to data using linear programming, Fuzzy Sets and Systems, 2022, in press, doi:10.1016/j.fss.2022.06.025

  4. A. Geras, G. Siudem, M. Gagolewski, Time to vote: Temporal clustering of user activity on Stack Overflow, Journal of the Association for Information Science and Technology, 2022, in press, doi:10.1002/asi.24658

  5. M. Gagolewski, B. Żogała-Siudem, G. Siudem, A. Cena, Ockham’s index of citation impact, Scientometrics 127, 2829–2845, 2022, doi:10.1007/s11192-022-04345-2

  6. A. Cena, M. Gagolewski, G. Siudem, B. Żogała-Siudem, Validating citation models by proxy indices, Journal of Informetrics 16(2), 101267, 2022, doi:10.1016/j.joi.2022.101267

  7. 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

  8. G. Beliakov, M. Gagolewski, S. James, Hierarchical data fusion processes involving the Möbius representation of capacities, Fuzzy Sets and Systems 433, 1–21, 2022, doi:10.1016/j.fss.2021.02.006

  9. 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

  10. J. Lasek, M. Gagolewski, 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

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

  12. 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

  13. M. Gagolewski, Lightweight Machine Learning Classics with R, open-access lecture notes, 2022, v0.2.3, doi:10.5281/zenodo.3679976, url:https://lmlcr.gagolewski.com/

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

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

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

  30. 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

  31. 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

  32. 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

  33. 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

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

  35. 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

  36. 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

  37. 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/

  38. M. Gagolewski, Algorytmy i postawy programowania w języku C++ (Introduction to Algorithms and Programming in C++), open-access lecture notes, Warsaw, Poland, 2016, 176 pp., in Polish, doi:10.5281/zenodo.6451055, url:https://github.com/gagolews/aipp

  39. 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

  40. 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

  41. 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

  42. 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

  43. 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

  44. 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

  45. 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

  46. 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

  47. 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

  48. 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

  49. 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

  50. 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

  51. M. Gagolewski, Data Fusion: Theory, Methods, and Applications, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, 2015, 290 pp.

  52. 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

  53. 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

  54. 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

  55. 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

  56. 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’2014, Vol. 2: Tools, Architectures, Systems, Applications, Springer, 2015, pp. 289–300, doi:10.1007/978-3-319-11310-4_25

  57. 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

  58. 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

  59. 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

  60. 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

  61. 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

  62. 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

  63. 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

  64. 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

  65. 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

  66. 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

  67. 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

  68. 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

  69. 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

  70. 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

  71. 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

  72. 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

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

  74. 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

  75. 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

  76. 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

  77. 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

  78. 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

  79. 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

  80. 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

  81. M. Gagolewski, P. Grzegorzewski, Arity-monotonic extended aggregation operators, in: E. Hüllermeier 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

  82. 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

  83. 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

  84. 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

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

  86. 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

  87. 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