Towards a minimum description length based stopping criterion for semi-supervised time series classification. - Nurjahan Begum; Bing Hu 001; Thanawin Rakthanmanon; Eamonn J. Keogh (IRI, 2013)

A Minimum Description Length Technique for Semi-Supervised Time Series Classification. - Nurjahan Begum; Bing Hu 001; Thanawin Rakthanmanon; Eamonn J. Keogh (IRI (best papers), 2013)

Rare Time Series Motif Discovery from Unbounded Streams. - Nurjahan Begum; Eamonn J. Keogh (Proc. VLDB Endow., 2014)

Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy. - Nurjahan Begum; Liudmila Ulanova; Jun Wang 037; Eamonn J. Keogh (KDD, 2015)

Scalable Clustering of Time Series with U-Shapelets. - Liudmila Ulanova; Nurjahan Begum; Eamonn J. Keogh (SDM, 2015)

Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping. - Hoang Anh Dau; Nurjahan Begum; Eamonn J. Keogh (CIKM, 2016)

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 014; Liudmila Ulanova; Nurjahan Begum; Yifei Ding; Hoang Anh Dau; Diego Furtado Silva; Abdullah Mueen; Eamonn J. Keogh (ICDM, 2016)

Clustering in the Face of Fast Changing Streams. - Liudmila Ulanova; Nurjahan Begum; Mohammad Shokoohi-Yekta; Eamonn J. Keogh (SDM, 2016)

A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy. - Nurjahan Begum; Liudmila Ulanova; Hoang Anh Dau; Jun Wang 037; Eamonn J. Keogh (CoRR, 2016)

Exploiting Time Series Primitives to Solve Realistic Data Mining Problems. - Nurjahan Begum (2016)

Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile. - Chin-Chia Michael Yeh; Yan Zhu 014; Liudmila Ulanova; Nurjahan Begum; Yifei Ding; Hoang Anh Dau; Zachary Zimmerman; Diego Furtado Silva; Abdullah Mueen; Eamonn J. Keogh (Data Min. Knowl. Discov., 2018)