Visualized Analysis of Mixed Numeric and Categorical Data Via Extended Self-Organizing Map

@article{Hsu2012VisualizedAO,
  title={Visualized Analysis of Mixed Numeric and Categorical Data Via Extended Self-Organizing Map},
  author={Chung-Chian Hsu and Shu-Han Lin},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2012},
  volume={23},
  pages={72-86}
}
Many real-world datasets are of mixed types, having numeric and categorical attributes. Even though difficult, analyzing mixed-type datasets is important. In this paper, we propose an extended self-organizing map (SOM), called MixSOM, which utilizes a data structure distance hierarchy to facilitate the handling of numeric and categorical values in a direct, unified manner. Moreover, the extended model regularizes the prototype distance between neighboring neurons in proportion to their map… CONTINUE READING
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