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

机译：通过扩展的自组织映射可视化分析数字和分类数据

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摘要

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 distance so that structures of the clusters can be portrayed better on the map. Extensive experiments on several synthetic and real-world datasets are conducted to demonstrate the capability of the model and to compare MixSOM with several existing models including Kohonen's SOM, the generalized SOM and visualization-induced SOM. The results show that MixSOM is superior to the other models in reflecting the structure of the mixed-type data and facilitates further analysis of the data such as exploration at various levels of granularity.
机译：许多现实世界的数据集是混合类型的，具有数字和类别属性。即使很困难，分析混合类型的数据集也很重要。在本文中，我们提出了一个扩展的自组织映射（SOM），称为MixSOM，它利用数据结构距离层次结构以直接，统一的方式简化了对数字和分类值的处理。此外，扩展模型将相邻神经元之间的原型距离与它们的地图距离成正比，从而可以在地图上更好地描绘簇的结构。在几个合成的和真实的数据集上进行了广泛的实验，以证明该模型的功能，并将MixSOM与包括Kohonen的SOM，广义SOM和可视化诱导的SOM在内的几种现有模型进行比较。结果表明，MixSOM在反映混合类型数据的结构方面优于其他模型，并有助于进一步分析数据，例如在各种粒度级别上进行勘探。

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