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Contains publications

• Artikkeli I: Hämäläinen, J., & Kärkkäinen, T. (2016). Initialization of Big Data Clustering using Distributionally Balanced Folding. In ESANN 2016 : Proceedings of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 587-592). ESANN. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-93.pdf
• Artikkeli II: Joonas Hämäläinen, Tommi Kärkkäinen and Tuomo Rossi. Scalable initial-ization methods for clustering large datasets. Pattern Recognition Letters (in revision), 2018
• Artikkeli III: Hämäläinen, J., Kärkkäinen, T., & Rossi, T. (2018). Scalable robust clustering method for large and sparse data. In ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 449-454). https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdf
• Artikkeli IV: Hämäläinen, J., Jauhiainen, S., & Kärkkäinen, T. (2017). Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering. Algorithms, 10 (3), 105. DOI: 10.3390/a10030105
• Artikkeli V: Saarela, M., Hämäläinen, J., & Kärkkäinen, T. (2017). Feature Ranking of Large, Robust, and Weighted Clustering Result. In K. Jinho, S. Kyuseok, C. Longbing, L. Jae-Gil, L. Xuemin, & M. Yang-Sae (Eds.), Advances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I (pp. 96-109). Springer International Publishing. DOI: 10.1007/978-3-319-57454-7_8
• Artikkeli VI: Joonas Hämäläinen, Tommi Kärkkäinen and João P. P. Gomes. Clustering-Based Reference Points Selection for the Minimal Learning Machine. Manuscript, 2018.