| Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle S Ruuska, W Hämäläinen, S Kajava, M Mughal, P Matilainen, J Mononen Behavioural processes 148, 56-62, 2018 | 277 | 2018 |
| Comparison of machine learning methods for intelligent tutoring systems W Hämäläinen, M Vinni International conference on intelligent tutoring systems, 525-534, 2006 | 198 | 2006 |
| Classifiers for educational data mining W Hämäläinen, M Vinni Handbook of Educational Data Mining, Chapman & Hall/CRC Data Mining and …, 2011 | 146* | 2011 |
| Data mining in personalizing distance education courses W Hämäläinen, TH Laine, E Sutinen Data mining in e-learning, 157-171, 2006 | 108 | 2006 |
| Efficient discovery of statistically significant association rules W Hämäläinen, M Nykänen 2008 Eighth IEEE international conference on data mining, 203-212, 2008 | 80 | 2008 |
| Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures W Hämäläinen Knowledge and information systems 32, 383-414, 2012 | 69 | 2012 |
| A tutorial on statistically sound pattern discovery W Hämäläinen, GI Webb Data Mining and Knowledge Discovery 32 (2), 325--377, 2019 | 62 | 2019 |
| Jerk-based feature extraction for robust activity recognition from acceleration data W Hamäläinen, M Järvinen, P Martiskainen, J Mononen 2011 11th International Conference on Intelligent Systems Design and …, 2011 | 59 | 2011 |
| StatApriori: an efficient algorithm for searching statistically significant association rules W Hämäläinen Knowledge and information systems 23, 373-399, 2010 | 55 | 2010 |
| Problem-based learning of theoretical computer science W Hämäläinen Kolin kolistelut Koli Calling 2003, Proceedings of the Third Finnish/Baltic …, 2003 | 40 | 2003 |
| Efficient discovery of the top-k optimal dependency rules with Fisher's exact test of significance W Hamalainen 2010 IEEE International Conference on Data Mining, 196-205, 2010 | 33 | 2010 |
| Measuring behaviour accurately with instantaneous sampling: A new tool for selecting appropriate sampling intervals W Hämäläinen, S Ruuska, T Kokkonen, S Orkola, J Mononen Applied Animal Behaviour Science 180, 166-173, 2016 | 23 | 2016 |
| New upper bounds for tight and fast approximation of Fisher’s exact test in dependency rule mining W Hämäläinen Computational statistics & data analysis 93, 469-482, 2016 | 19 | 2016 |
| Metabolomics of synovial fluid and infrapatellar fat pad in patients with osteoarthritis or rheumatoid arthritis P Nieminen, W Hämäläinen, J Savinainen, M Lehtonen, S Lehtiniemi, ... Inflammation 45 (3), 1101-1117, 2022 | 17 | 2022 |
| Can stealing cows distort the results of feeding trials? An experiment for quantification and prevention of stealing feed by dairy cows from roughage intake control feeders S Ruuska, W Hämäläinen, A Sairanen, E Juutinen, L Tuomisto, ... Applied Animal Behaviour Science 159, 1-8, 2014 | 15 | 2014 |
| Class NP, NP-complete, and NP-hard problems W Hämäläinen Sort, 1-7, 2006 | 15 | 2006 |
| Evaluation of clustering methods for adaptive learning systems W Hämäläinen, V Kumpulainen, M Mozgovoy Artificial Intelligence Applications in Distance Education, 237-260, 2014 | 13 | 2014 |
| Efficient search for statistically significant dependency rules in binary data W Hämäläinen Helsingin yliopisto, 2010 | 12 | 2010 |
| Statistically sound pattern discovery. W Hämäläinen, GI Webb KDD, 1976, 2014 | 11 | 2014 |
| Descriptive and predictive modelling techniques for educational technology W Hämäläinen Licentiate thesis, Department of Computer Science, University of Joensuu, 2006 | 10 | 2006 |
Geoff WebbProfessor, Department of Data Science and Artificial Intelligence, Monash University在 monash.edu 的电子邮件经过验证
Geoff WebbProfessor, Department of Data Science and Artificial Intelligence, Monash University在 monash.edu 的电子邮件经过验证
Maxim MozgovoyThe University of Aizu在 u-aizu.ac.jp 的电子邮件经过验证