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Visualization of trends using RadViz

Abstract

Data mining is sometimes treating data consisting of items representing measurements of a single property taken in different time points. In this case data can be understood as a time series of one feature. It is no exception when the clue for evaluation of such data is related to their development trends as observed in several successive time points. From the qualitative point of view one can distinguish three basic types of behaviour between two neighbouring time points: the value of the feature is stable (remains the same), it grows or it falls. This paper is concerned with identification of typical qualitative development patterns as they appear in the windows of given length in the considered time-stamped data and their utilization for specification of interesting subgroups.

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Notes

  1. The STULONG study was performed at the 2nd Dept. of Internal Medicine of the 1st Medical Faculty of the Charles University and General Faculty Hospital in Prague 2 (initially General Medical Faculty of the Charles University and Faculty Hospital I) managed by clinicians prof. MUDr. F.Boudik, DrSc., MUDr. M.Tomeckova, CSc. and doc. MUDr. J.Bultas, CSc. Most of the data was transferred into the electronic form by EuroMISE (European Centre for Medical Informatics, Statistics and Epidemiology) with support of European project (http://euromise.vse.cz/stulong).

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Acknowledgements

The first author was supported by the grant MSM 6840770038 Decision Making and Control for Manufacturing III of the Czech Ministry of Education, Youth and Sports and the second author was supported by the grant 1ET101210513 (Relational Machine Learning for Biomedical Data Analysis).

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Correspondence to Lenka Nováková.

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Nováková, L., Štěpánková, O. Visualization of trends using RadViz. J Intell Inf Syst 37, 355 (2011). https://doi.org/10.1007/s10844-011-0157-4

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  • DOI: https://doi.org/10.1007/s10844-011-0157-4

Keywords

  • Time series
  • Data visualization
  • RadViz