Nurjahan Begum

According to our database, Nurjahan Begum authored at least 11 papers between 2013 and 2018.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2018

Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile.

Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Zachary Zimmerman, Diego Furtado Silva, Abdullah Mueen, Eamonn J. Keogh

Data Min. Knowl. Discov., 2018

2016

Exploiting Time Series Primitives to Solve Realistic Data Mining Problems.

Nurjahan Begum

PhD thesis, 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, Eamonn J. Keogh

CoRR, 2016

Clustering in the Face of Fast Changing Streams.

Liudmila Ulanova, Nurjahan Begum, Mohammad Shokoohi-Yekta, Eamonn J. Keogh

Proceedings of the 2016 SIAM International Conference on Data Mining, 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, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, Eamonn J. Keogh

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping.

Hoang Anh Dau, Nurjahan Begum, Eamonn J. Keogh

Proceedings of the 25th ACM International Conference on Information and Knowledge Management, 2016

2015

Scalable Clustering of Time Series with U-Shapelets.

Liudmila Ulanova, Nurjahan Begum, Eamonn J. Keogh

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy.

Nurjahan Begum, Liudmila Ulanova, Jun Wang, Eamonn J. Keogh

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014

Rare Time Series Motif Discovery from Unbounded Streams.

Nurjahan Begum, Eamonn J. Keogh

Proc. VLDB Endow., 2014

2013

A Minimum Description Length Technique for Semi-Supervised Time Series Classification.

Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh

Proceedings of the Integration of Reusable Systems [extended versions of the best papers which were presented at IEEE International Conference on Information Reuse and Integration and IEEE International Workshop on Formal Methods Integration, 2013

Towards a minimum description length based stopping criterion for semi-supervised time series classification.

Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh

Proceedings of the IEEE 14th International Conference on Information Reuse & Integration, 2013