GViSOM for Multivariate Mixed Data Projection and Structure Visualization

Data mining has become a popular technology in analyzing complex data. Clustering is one of the data mining core techniques. In this paper, we propose a new visualized clustering approach, namely generalized visualization-induced self-organizing map (GViSOM), to cluster mixed, numeric and categorical, data. Our model integrates the ideas from SOM, GSOM, and ViSOM, and overcomes their shortcomings, including projection distortion on the maps and the incapability of handling mixed data. GViSOM can directly handle mixed data and preserves the topological structure of the original data as faithfully as possible. Experimental results of a synthetic and a real datasets demonstrate that GViSOM is able to cluster mixed data and better reveals the cluster structure than SOM, GSOM and ViSOM.