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

• Published in: IEEE transaction on neural networks and learning systems Vol. 23; no. 1; pp. 72 - 86
• Main Authors: HSU, Chung-Chian, LIN, Shu-Han
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerospace electronics; Algorithm design and analysis; Applied sciences; Artificial intelligence; Categorical data analysis; clustering methods; Computer science; control theory; systems; Connectionism. Neural networks; Data visualization; Exact sciences and technology; mixed-type data analysis; Neurons; Numerical models; Prototypes; self-organizing map; Studies; Training; visualization; Grain size; Capability index; clustering methods; Data type; Visualization; Data analysis; Categorical data analysis; Cluster; Neural network; Kohonen algorithm; Modeling; Categorical data; Self organization; self-organizing map; mixed-type data analysis; Data structure
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2011.2178323

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.