| Comparison of internal clustering validation indices for prototype-based clustering J Hämäläinen, S Jauhiainen, T Kärkkäinen Algorithms 10 (3), 105, 2017 | 150 | 2017 |
| A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles S Malola, P Nieminen, A Pihlajamäki, J Hämäläinen, T Kärkkäinen, ... Nature communications 10 (1), 3973, 2019 | 45 | 2019 |
| Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods A Pihlajamaki, J Hamalainen, J Linja, P Nieminen, S Malola, ... The Journal of Physical Chemistry A 124 (23), 4827-4836, 2020 | 42 | 2020 |
| Improving scalable K-means++ J Hämäläinen, T Kärkkäinen, T Rossi Algorithms 14 (1), 6, 2020 | 28 | 2020 |
| Minimal learning machine: Theoretical results and clustering-based reference point selection J Hämäläinen, ASC Alencar, T Kärkkäinen, CLC Mattos, AHS Júnior, ... Journal of Machine Learning Research 21 (239), 1-29, 2020 | 22 | 2020 |
| Feature selection for distance-based regression: An umbrella review and a one-shot wrapper J Linja, J Hämäläinen, P Nieminen, T Kärkkäinen Neurocomputing 518, 344-359, 2023 | 13 | 2023 |
| Feature ranking of large, robust, and weighted clustering result M Saarela, J Hämäläinen, T Kärkkäinen Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia …, 2017 | 12 | 2017 |
| Mapping the challenges of HCI: An application and evaluation of ChatGPT and GPT-4 for cost-efficient question answering J Oppenlaender, J Hämäläinen arXiv preprint arXiv:2306.05036, 2023 | 8 | 2023 |
| Scalable robust clustering method for large and sparse data J Hämäläinen, T Kärkkäinen, T Rossi European Symposium on Artificial Neural Networks, Computational Intelligence …, 2018 | 8 | 2018 |
| Do randomized algorithms improve the efficiency of minimal learning machine? J Linja, J Hämäläinen, P Nieminen, T Kärkkäinen Machine Learning and Knowledge Extraction 2 (4), 533-557, 2020 | 7 | 2020 |
| Initialization of big data clustering using distributionally balanced folding. J Hämäläinen, T Kärkkäinen ESANN, 2016 | 4 | 2016 |
| Instance-based multi-label classification via multi-target distance regression J Hämäläinen, P Nieminen, T Kärkkäinen European Symposium on Artificial Neural Networks, Computational Intelligence …, 2021 | 3 | 2021 |
| Scalable initialization methods for large-scale clustering J Hämäläinen, T Kärkkäinen, T Rossi arXiv preprint arXiv:2007.11937, 2020 | 3 | 2020 |
| Improvements and applications of the elements of prototype-based clustering J Hämäläinen JYU dissertations, 2018 | 3 | 2018 |
| Newton Method for Minimal Learning Machine J Hämäläinen, T Kärkkäinen Computational Sciences and Artificial Intelligence in Industry: New Digital …, 2022 | 2 | 2022 |
| Problem transformation methods with distance-based learning for multi-target regression J Hämäläinen, T Kärkkäinen European Symposium on Artificial Neural Networks, Computational Intelligence …, 2020 | 2 | 2020 |
| Minimal Learning Machine for Multi-Label Learning J Hämäläinen, A Souza, CLC Mattos, JPP Gomes, T Kärkkäinen arXiv preprint arXiv:2305.05518, 2023 | 1 | 2023 |
| Knowledge Discovery from Atomic Structures using Feature Importances J Linja, J Hämäläinen, A Pihlajamäki, P Nieminen, S Malola, H Häkkinen, ... arXiv preprint arXiv:2303.09453, 2023 | 1 | 2023 |
| Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces A Pihlajamäki, J Linja, J Hämäläinen, P Nieminen, S Malola, ... European Symposium on Artificial Neural Networks, Computational Intelligence …, 2021 | 1 | 2021 |
| Au38Q MBTR-K3 J Linja, T Kärkkäinen, J Hämäläinen, P Nieminen Zenodo, 2023 | | 2023 |
