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Self-Organization Map (SOM) offers an effective visualization capability for analyzing high-dimensional data. Nevertheless, most SOM models lack a robust solution to appropriately manipulate both numeric and categorical data. To solve the foregoing problem, Generalized SOM (GenSOM) was proposed to handle distance measurement of mixed-type data via distance hierarchy. Whereas GenSOM constrains projection result in a predetermined fixed-size map, making the resultant map unable to reflect data distribution in accordance with the nature of data clusters. In this paper, we propose a Growing Mixed-type SOM (GMixSOM) which extends GenSOM with a dynamic structure, to handle mixed-type data and tackle the problem of fixed map structure of GenSOM. Experimental results show the proposed method can reveal topological relationship between mixed data and overcome the drawback of map structure constraint arisen in GenSOM.
Date of Conference: 18-23 July 2010