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Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns

Peter Sarlin, Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns. Neurocomputing 99(1), 496–508, 2013.

http://dx.doi.org/10.1016/j.neucom.2012.07.011

Abstract:

This paper adopts and adapts Kohonen’s standard self-organizing map (SOM) for exploratory temporal structure analysis. The self-organizing time map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The two-dimensional representation of the SOTM attempts thus twofold topology preservation, where the horizontal direction preserves time topology and the vertical direction data topology. This enables discovering the occurrence and exploring the properties of temporal structural changes in data. For representing qualities and properties of SOTMs, we adapt measures and visualizations from the standard SOM paradigm, as well as introduce a measure of temporal structural changes. The functioning of the SOTM, and its visualizations and quality and property measures, are illustrated on artificial toy data. The usefulness of the SOTM in a real-world setting is shown on poverty, welfare and development indicators.

BibTeX entry:

@ARTICLE{jSarlin_Peter13a,
  title = {Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns},
  author = {Sarlin, Peter},
  journal = {Neurocomputing},
  volume = {99},
  number = {1},
  publisher = {Elsevier},
  pages = {496–508},
  year = {2013},
  keywords = {Self-organizing time map; Self-organizing map; Exploratory temporal structure analysis; Dynamic visual clustering; Exploratory data analysis},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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