| Mining frequent itemsets using the N-list and subsume concepts B Vo, T Le, F Coenen, TP Hong International Journal of Machine Learning and Cybernetics 7 (2), 253-265, 2016 | 76 | 2016 |
| An efficient and effective algorithm for mining top-rank-k frequent patterns Q Huynh-Thi-Le, T Le, B Vo, B Le Expert Systems with Applications 42 (1), 156-164, 2015 | 53 | 2015 |
| MEI: an efficient algorithm for mining erasable itemsets T Le, B Vo Engineering Applications of Artificial Intelligence 27, 155-166, 2014 | 51 | 2014 |
| An N-list-based algorithm for mining frequent closed patterns T Le, B Vo Expert Systems with Applications 42 (19), 6648-6657, 2015 | 40 | 2015 |
| A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset T Le, HS Le, MT Vo, MY Lee, SW Baik Symmetry 10 (7), 250, 2018 | 39* | 2018 |
| An efficient algorithm for mining erasable itemsets using the difference of NC-Sets T Le, B Vo, F Coenen 2013 IEEE International Conference on Systems, Man, and Cybernetics, 2270-2274, 2013 | 39 | 2013 |
| Oversampling techniques for bankruptcy prediction: novel features from a transaction dataset T Le, MY Lee, JR Park, SW Baik Symmetry 10 (4), 79, 2018 | 34 | 2018 |
| A Novel Approach for Mining Maximal Frequent Patterns B Vo, S Pham, T Le, ZH Deng Expert Systems with Applications 73, 178-186, 2017 | 32 | 2017 |
| Improving electric energy consumption prediction using CNN and Bi-LSTM T Le, MT Vo, B Vo, E Hwang, S Rho, SW Baik Applied Sciences 9 (20), 4237, 2019 | 27 | 2019 |
| Mining top-k co-occurrence items with sequential pattern T Kieu, B Vo, T Le, ZH Deng, B Le Expert Systems with Applications 85, 123-133, 2017 | 24 | 2017 |
| An effective approach for maintenance of pre-large-based frequent-itemset lattice in incremental mining B Vo, T Le, TP Hong, B Le Applied Intelligence 41 (3), 759-775, 2014 | 24 | 2014 |
| Efficient algorithms for mining top-rank-k erasable patterns using pruning strategies and the subsume concept T Le, B Vo, SW Baik Engineering Applications of Artificial Intelligence 68, 1-9, 2018 | 23 | 2018 |
| Race recognition using deep convolutional neural networks T Vo, T Nguyen, CT Le Symmetry 10 (11), 564, 2018 | 22 | 2018 |
| Mining erasable itemsets with subset and superset itemset constraints B Vo, T Le, W Pedrycz, G Nguyen, SW Baik Expert Systems with Applications 69, 50-61, 2017 | 22 | 2017 |
| A hybrid approach for mining frequent itemsets B Vo, F Coenen, T Le, TP Hong 2013 IEEE International Conference on Systems, Man, and Cybernetics, 4647-4651, 2013 | 22 | 2013 |
| The lattice‐based approaches for mining association rules: a review T Le, B Vo Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6 (4 …, 2016 | 21 | 2016 |
| A New Approach for Mining Top-Rank-k Erasable Itemsets G Nguyen, T Le, B Vo, B Le Asian Conference on Intelligent Information and Database Systems, 73-82, 2014 | 19 | 2014 |
| A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting T Le, B Vo, H Fujita, NT Nguyen, SW Baik Information Sciences 494, 294-310, 2019 | 18 | 2019 |
| EIFDD: An efficient approach for erasable itemset mining of very dense datasets G Nguyen, T Le, B Vo, B Le Applied Intelligence 43 (1), 85-94, 2015 | 15 | 2015 |
| A survey of erasable itemset mining algorithms T Le, B Vo, G Nguyen Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4 (5 …, 2014 | 15 | 2014 |
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