Authors
Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold P Boedihardjo, Crystal Chen, Susan Frankenstein, Manfred Lerner
Publication date
2014
Conference
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III 14
Pages
468-472
Publisher
Springer Berlin Heidelberg
Description
The problem of frequent and anomalous patterns discovery in time series has received a lot of attention in the past decade. Addressing the common limitation of existing techniques, which require a pattern length to be known in advance, we recently proposed grammar-based algorithms for efficient discovery of variable length frequent and rare patterns. In this paper we present GrammarViz 2.0, an interactive tool that, based on our previous work, implements algorithms for grammar-driven mining and visualization of variable length time series patterns1.
Scholar articles
P Senin, J Lin, X Wang, T Oates, S Gandhi… - Machine Learning and Knowledge Discovery in …, 2014