Visual discovery and model-driven explanation of time series patterns
Proceedings of the 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
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Sarkar, A., Spott, M., Blackwell, A., & Jamnik, M. (2016). Visual discovery and model-driven explanation of time series patterns. Proceedings of the 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) https://doi.org/10.1109/VLHCC.2016.7739668
Gatherminer is an interactive visual tool for analysing time series data with two key strengths. First, it facilitates bottom-up analysis, i.e., the detection of trends and patterns whose shapes are not known beforehand. Second, it integrates data mining algorithms to explain such patterns in terms of the time series’ metadata attributes – an extremely difficult task if the space of attribute-value combinations is large. To accomplish these aims, Gatherminer automatically rearranges the data to visually expose patterns and clusters, whereupon users can select those groups they deem ‘interesting.’ To explain the selected patterns, the visualisation is tightly coupled with automated classification techniques, such as decision tree learning. We present a brief evaluation with telecommunications experts comparing our tool against their current commercial solution, and conclude that Gatherminer significantly improves both the completeness of analyses as well as analysts’ confidence therein.
Advait is supported by an EPSRC+BT iCASE award and a Cambridge Computer Laboratory Robert Sansom scholarship.
External DOI: https://doi.org/10.1109/VLHCC.2016.7739668
This record's URL: https://www.repository.cam.ac.uk/handle/1810/260285