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Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series

Peter Sarlin, Tomas Eklund, Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series. In: Jorma Laaksonen, Timo Honkela (Eds.), Proceedings of the 8th International Workshop on Self-Organizing Maps (wSOM'11), 40–50, Springer, 2011.

Abstract:

The Self-organizing map (SOM) has been widely used in financial applications, not least for time-series analysis. The SOM has not only been utilized as a stand-alone clustering technique, its output has also been used as input for second-stage clustering. However, one ambiguity with the SOM clustering is that the degree of membership in a particular cluster is not always easy to judge. To this end, we propose a fuzzy C-means clustering of the units of two previously presented SOM models for financial time-series analysis: financial benchmarking of companies and monitoring indicators of currency crises. It allows each time-series point to have a partial membership in all identified, but overlapping, clusters, where the cluster centers express the representative financial states for the companies and countries, while the fluctuations of the membership degrees represent their variations over time.

BibTeX entry:

@INPROCEEDINGS{iSaEk11a,
  title = {Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series},
  booktitle = {Proceedings of the 8th International Workshop on Self-Organizing Maps (wSOM'11)},
  author = {Sarlin, Peter and Eklund, Tomas},
  editor = {Laaksonen, Jorma and Honkela, Timo},
  publisher = {Springer},
  pages = {40–50},
  year = {2011},
  keywords = {Self-organizing maps; fuzzy C-means; financial time series},
}

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

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