| 2016 | CIKM | Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping. | Hoang Anh Dau, Nurjahan Begum, Eamonn J. Keogh |
| 2016 | ICDM | Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets. | Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, Eamonn J. Keogh |
| 2016 | SDM | Clustering in the Face of Fast Changing Streams. | Liudmila Ulanova, Nurjahan Begum, Mohammad Shokoohi-Yekta, Eamonn J. Keogh |
| 2015 | KDD | Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy. | Nurjahan Begum, Liudmila Ulanova, Jun Wang, Eamonn J. Keogh |
| 2015 | SDM | Scalable Clustering of Time Series with U-Shapelets. | Liudmila Ulanova, Nurjahan Begum, Eamonn J. Keogh |
| 2013 | IRI | Towards a minimum description length based stopping criterion for semi-supervised time series classification. | Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh |
| 2013 | IRI | A Minimum Description Length Technique for Semi-Supervised Time Series Classification. | Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh |