| Generalized random shapelet forests I Karlsson, P Papapetrou, H Boström Data mining and knowledge discovery 30, 1053-1085, 2016 | 158 | 2016 |
| A peek into the black box: exploring classifiers by randomization A Henelius, K Puolamäki, H Boström, L Asker, P Papapetrou Data mining and knowledge discovery 28, 1503-1529, 2014 | 150 | 2014 |
| Nearest neighbor retrieval using distance-based hashing V Athitsos, M Potamias, P Papapetrou, G Kollios 2008 IEEE 24th International Conference on Data Engineering, 327-336, 2008 | 142 | 2008 |
| Discovering frequent arrangements of temporal intervals P Papapetrou, G Kollios, S Sclaroff, D Gunopulos Fifth IEEE International Conference on Data Mining (ICDM'05), 8 pp., 2005 | 132 | 2005 |
| Mining frequent arrangements of temporal intervals P Papapetrou, G Kollios, S Sclaroff, D Gunopulos Knowledge and Information Systems 21, 133-171, 2009 | 129 | 2009 |
| Significance testing of word frequencies in corpora J Lijffijt, T Nevalainen, T Säily, P Papapetrou, K Puolamäki, H Mannila Digital Scholarship in the Humanities 31 (2), 374-397, 2016 | 114 | 2016 |
| Learning from heterogeneous temporal data in electronic health records J Zhao, P Papapetrou, L Asker, H Boström Journal of biomedical informatics 65, 105-119, 2017 | 100 | 2017 |
| Approximate embedding-based subsequence matching of time series V Athitsos, P Papapetrou, M Potamias, G Kollios, D Gunopulos Proceedings of the 2008 ACM SIGMOD international conference on Management of …, 2008 | 96 | 2008 |
| Embedding-based subsequence matching in time-series databases P Papapetrou, V Athitsos, M Potamias, G Kollios, D Gunopulos ACM Transactions on Database Systems (TODS) 36 (3), 1-39, 2011 | 92 | 2011 |
| Seq2Seq RNNs and ARIMA models for cryptocurrency prediction: A comparative study J Rebane, I Karlsson, P Papapetrou, S Denic SIGKDD Fintech’18, London, UK, August 19-23, 2018, 2018 | 86 | 2018 |
| Cellular traffic prediction and classification: A comparative evaluation of LSTM and ARIMA A Azari, P Papapetrou, S Denic, G Peters Discovery Science: 22nd International Conference, DS 2019, Split, Croatia …, 2019 | 77 | 2019 |
| A statistical significance testing approach to mining the most informative set of patterns J Lijffijt, P Papapetrou, K Puolamäki Data Mining and Knowledge Discovery 28, 238-263, 2014 | 42 | 2014 |
| Evaluating local interpretable model-agnostic explanations on clinical machine learning classification models NB Kumarakulasinghe, T Blomberg, J Liu, AS Leao, P Papapetrou 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems …, 2020 | 41 | 2020 |
| A survey of query-by-humming similarity methods A Kotsifakos, P Papapetrou, J Hollmén, D Gunopulos, V Athitsos Proceedings of the 5th International Conference on PErvasive Technologies …, 2012 | 41 | 2012 |
| Artemis: Assessing the similarity of event-interval sequences O Kostakis, P Papapetrou, J Hollmén Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 37 | 2011 |
| A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records F Bagattini, I Karlsson, J Rebane, P Papapetrou BMC medical informatics and decision making 19, 1-20, 2019 | 36 | 2019 |
| Explainable time series tweaking via irreversible and reversible temporal transformations I Karlsson, J Rebane, P Papapetrou, A Gionis 2018 IEEE International Conference on Data Mining (ICDM), 207-216, 2018 | 36 | 2018 |
| User traffic prediction for proactive resource management: Learning-powered approaches A Azari, P Papapetrou, S Denic, G Peters 2019 IEEE Global Communications Conference (GLOBECOM), 1-6, 2019 | 34 | 2019 |
| Reference-based alignment in large sequence databases P Papapetrou, V Athitsos, G Kollios, D Gunopulos Proceedings of the VLDB Endowment 2 (1), 205-216, 2009 | 34 | 2009 |
| IBSM: Interval-based sequence matching A Kotsifakos, P Papapetrou, V Athitsos Proceedings of the 2013 SIAM International Conference on Data Mining, 596-604, 2013 | 30 | 2013 |
Henrik BoströmProfessor of computer science - data science systems, KTH Royal Institute of Technology在 kth.se 的電子郵件地址已通過驗證
