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Root Cause Detection using Dynamic Dependency Graphs from Time Series Data.

, , and . IEEE BigData, page 1998-2003. IEEE, (2018)

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seq2graph: Discovering Dynamic Non-linear Dependencies from Multivariate Time Series., , and . IEEE BigData, page 1774-1783. IEEE, (2019)Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data., , , , and . CoRR, (2015)Frequent Sets Mining in Data Stream Environments., , , and . Encyclopedia of Data Warehousing and Mining, IGI Global, (2009)Subnetwork Mining with Spatial and Temporal Smoothness., , , and . SDM, page 354-362. SIAM, (2017)Root Cause Detection using Dynamic Dependency Graphs from Time Series Data., , and . IEEE BigData, page 1998-2003. IEEE, (2018)Unsupervised Threshold Autoencoder to Analyze and Understand Sentence Elements., , , , and . IEEE BigData, page 3267-3276. IEEE, (2018)Learning Predictive Substructures with Regularization for Network Data., , , and . ICDM, page 81-90. IEEE Computer Society, (2015)Approximation algorithms for mining patterns from data streams. Nanyang Technological University, Singapore, (2008)An Adaptive Algorithm for Finding Frequent Sets in Landmark Windows., , and . SUM, volume 7520 of Lecture Notes in Computer Science, page 590-597. Springer, (2012)Incremental and Adaptive Clustering Stream Data over Sliding Window., , , and . DEXA, volume 5690 of Lecture Notes in Computer Science, page 660-674. Springer, (2009)