TimeCluster: Dimension Reduction applied to Temporal Data for Visual Analytics

Mohammed Ali, Mark W. Jones, Xianghua Xie and Mark Williams

Crynodeb

There is a need for solutions which assist users to understand long time-series data by observing its changes over time, finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct, and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system.

Deunyddiau Ffynhonnell

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DOI

10.1007/s00371-019-01673-y
https://dx.doi.org/10.1007/s00371-019-01673-y

Enwi

Mohammed Ali, Mark W. Jones, Xianghua Xie and Mark Williams, TimeCluster: Dimension Reduction applied to Temporal Data for Visual Analytics. The Visual Computer 35(6-8), 1013-1026. 2019. https://doi.org/10.1007/s00371-019-01673-y

Bibtex

@article{Time_Cluster2019, 
author={Mohammed Ali and Mark W. Jones and Xianghua Xie and Mark Williams}, 
title={TimeCluster: Dimension Reduction applied to Temporal Data for Visual Analytics}, 
journal={The Visual Computer}, 
author="Ali, Mohammed
and Jones, Mark W.
and Xie, Xianghua
and Williams, Mark",
title="TimeCluster: dimension reduction applied to temporal data for visual analytics",
year="2019",
month="Jun",
day="01",
volume="35",
number="6",
pages="1013--1026",
issn="1432-2315",
doi="10.1007/s00371-019-01673-y",
url="https://doi.org/10.1007/s00371-019-01673-y"
}