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On Visual Exploration of Breast Cancer Data Using the Self-Organizing Map

Tomas Eklund, Mikael Collan, Päivi Jalava, Teuvo Kuopio, Yrjö Collan, On Visual Exploration of Breast Cancer Data Using the Self-Organizing Map. In: Marie Cotreel (Ed.), Proceedings of the 5th Workshop on Self-Organizing Maps (WSOM’05), 139–146, WSOM, 2005.

Abstract:

This paper explores the use of self-organizing maps (SOM) for exploratory data analysis of breast cancer data. We were able to visualize the data with the SOM in a way that makes it possible to, rather easily, identify possible connections between variables. The possible connections found can then be further tested for statistical significance, using wellknown statistical methods. We report preliminary results on using SOM for identifying possible relationships in breast cancer data. The results are consistent with the existing scientific medical literature on breast cancer. We discuss the usability of the SOM for finding patterns and connections between variables in medical data.

BibTeX entry:

@INPROCEEDINGS{inpEkCoJaKuCo05a,
  title = {On Visual Exploration of Breast Cancer Data Using the Self-Organizing Map},
  booktitle = {Proceedings of the 5th Workshop on Self-Organizing Maps (WSOM’05)},
  author = {Eklund, Tomas and Collan, Mikael and Jalava, Päivi and Kuopio, Teuvo and Collan, Yrjö},
  editor = {Cotreel, Marie},
  publisher = {WSOM},
  pages = {139–146},
  year = {2005},
  keywords = {Self-Organizing map, breast cancer, exploratory data analysis},
}

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

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