Processing Missing Values with Self-Organized Maps
نویسندگان
چکیده
This paper introduces modifications of Self-Organizing Maps allowing imputation and classification of data containing missing values. The robustness of the proposed modifications is shown using experimental results of a standard data set. A comparison to modified Fuzzy cluster methods [Timm et al., 2002] is presented. Both methods performed better with available case analysis compared to complete case analysis. Further modifications of the SOM using k-nearest neighbor calculations result in lower classification errors and lower variances of classification errors.
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