Visual discovery and model-driven explanation of time series patterns
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Publication Date
2016Journal Title
Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC
Conference Name
2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
ISSN
1943-6092
ISBN
9781509002528
Publisher
IEEE
Language
English
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Sarkar, A., Spott, M., Blackwell, A., & Jamnik, M. (2016). Visual discovery and model-driven explanation of time series patterns. Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC https://doi.org/10.1109/VLHCC.2016.7739668
Abstract
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.
Sponsorship
Advait is supported by an EPSRC+BT iCASE award and a
Cambridge Computer Laboratory Robert Sansom scholarship.
Identifiers
External DOI: https://doi.org/10.1109/VLHCC.2016.7739668
This record's URL: https://www.repository.cam.ac.uk/handle/1810/260285
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