Benchmark Suite for Clustering Algorithms

Let’s aggregate, polish and standardise the existing clustering benchmark suites referred to across the machine learning and data mining literature! See our new Benchmark Suite for Clustering Algorithms.


  • Co-investigator in Research Project 2014/13/D/HS4/01700 (NCN) Construction and analysis of methods of information resources producers’ quality management (2015-2017)
  • Participant in Interdisciplinary Ph.D. Studies Program Information technologies: Research and their interdisciplinary application, ICS PAS, SRI PAS, IBIB PAS (2013-2015)



  1. Gagolewski M., Bartoszuk M., Cena A., Przetwarzanie i analiza danych w języku Python, (Data processing and analysis in Python), Wydawnictwo Naukowe PWN, 2016, pp. 369, ISBN: 978-83-01-18940-2.

Articles in Journals

  1. 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(25), 2020, pp. 13896-13900. doi:10.1073/pnas.2001064117
  2. Cena A., Gagolewski M., Genie+OWA: Robustifying Hierarchical Clustering with OWA-based Linkages, Information Sciences, 2020, in press. doi:10.1016/j.ins.2020.02.025
  3. Markiewicz J., Pilarska M., Łapiński S., Kaliszewska A., Bieńkowski R., Cena A., Quality assessment of the use of a medium format camera in the investigation of wall paintings: An image-based approach, Measurement 132, 2019, pp. 224–237.
  4. Gagolewski M., Bartoszuk M., Cena A., Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm, Information Sciences 363, 2016, pp. 8-23.
  5. Żogała-Siudem B., Siudem G., Cena A., Gagolewski M., Agent-based model for the h-index – Exact solution, European Physical Journal B 89:21, 2016.
  6. Cena A., Gagolewski M., Mesiar R., Problems and challenges of information resources producers’ clustering, Journal of Informetrics 9(2), 2015, pp. 273–284.
    [ Scopus 2011 ] [ Scopus 2011 (sample) ] [ stats 2014 ]
  7. Cena A., Gagolewski M., OM3: Ordered maxitive, minitive, and modular aggregation operators — axiomatic and probabilistic properties in an arity-monotonic setting, Fuzzy Sets and Systems 264, 2015, pp. 138-159.

Papers in Edited Volumes and Proceedings

  1. Cena A., Gagolewski M., OWA-based linkage and the Genie correction for hierarchical clustering, In: Proc. FUZZ-IEEE’17, IEEE, 2017
  2. Gagolewski M., Cena A., Bartoszuk M., Hierarchical clustering via penalty-based aggregation and the Genie approach, In: Torra V. et al. (Eds.), Modeling Decisions for Artificial Intelligence (Lecture Notes in Artificial Intelligence 9880), Springer, 2016, pp. 191-202.
  3. Cena A., Gagolewski M., Fuzzy K-minpen clustering and K-nearest-minpen classification procedures incorporating generic distance-based penalty minimizers, In: Carvalho J.P. et al. (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II (Communications in Computer and Information Science 611), Springer, 2016, pp. 445-456.          [ Scopus data]
  4. Cena A., Gagolewski M., Clustering and aggregation of informetric data sets, In: Computational Methods in Data Analysis (Proc. ITRIA’15 vol. 1), IPI PAN, Warszawa, 2015, pp. 5–26.
  5. Cena A., Gagolewski M., Aggregation and soft clustering of informetric data, In: Baczyński M., De Baets B., Mesiar R. (Eds.), Proc. 8th International Summer School on Aggregation Operators (AGOP 2015), University of Silesia, ISBN:978-83-8012-519-3, 2015, pp. 79-84.
  6. Cena A., Gagolewski M., A K-means-like algorithm for informetric data clustering, In: Alonso J.M., Bustince H., Reformat M. (Eds.), Proc. IFSA/EUSFLAT 2015, Atlantic Press, 2015, pp. 536-543.
  7. Cena A., Gagolewski M., OM3: Ordered Maxitive, Minitive, and Modular Aggregation Operators — Part II: A simulation study, In: Bustince H. et al (Eds.), Aggregation Functions in Theory and in Practise (AISC 228), Springer-Verlag, Heidelberg, 2013, pp. 105-115.
  8. Cena A., Gagolewski M., OM3: ordered maxitive, minitive, and modular aggregation operators – Part I: Axiomatic analysis under arity-dependence, In: Bustince H. et al (Eds.), Aggregation Functions in Theory and in Practise (AISC 228), Springer-Verlag, Heidelberg, 2013, pp. 93-103.