I want to use the NEWSOM neural network for clustering. My dataset contains both numerical and categorical variables; in the documentation, however, I can only find how to work with numerical variables.
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The ability to work with categorical variables is not available in the Neural Network Toolbox. It is supported with the Statistics Toolbox. To work around this issue, you need to represent your categories as numerical values. In general there are two approaches: 1. Represent each category as an integer. For example if you have categories 'small', 'medium' and 'large' you could say 'small' = 0, 'medium' = 1, 'large' = 2. Although in general this idea should work fine when you only have categorical variables, you may need to pay attention to scaling when you also have numerical variables. The idea is that a numeric change for one input of the net (for example integers representing one of your categorical variables) should have roughly the same importance as the same numeric change to another input (for example one of your numerical variables). There are several tools in the Neural Network Toolbox that help you scale your inputs, see the "Processing Functions" section in the Neural Network Toolbox documentation. Normally start with MAPMINMAX if this does not perform well you could try MAPSTD or possibly PROCESSPCA. You can apply these functions manually, or you can apply them automatically to an input by setting the 'processFcns' property for the input.
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The ability to work with categorical variables is not available in the Neural Network Toolbox. It is supported with the Statistics Toolbox.
To work around this issue, you need to represent your categories as numerical values. In general there are two approaches:
1. Represent each category as an integer. For example if you have categories 'small', 'medium' and 'large' you could say 'small' = 0, 'medium' = 1, 'large' = 2. Although in general this idea should work fine when you only have categorical variables, you may need to pay attention to scaling when you also have numerical variables. The idea is that a numeric change for one input of the net (for example integers representing one of your categorical variables) should have roughly the same importance as the same numeric change to another input (for example one of your numerical variables). There are several tools in the Neural Network Toolbox that help you scale your inputs, see the "Processing Functions" section in the Neural Network Toolbox documentation.
Normally start with MAPMINMAX if this does not perform well you could try MAPSTD or possibly PROCESSPCA. You can apply these functions manually, or you can apply them automatically to an input by setting the 'processFcns' property for the input.
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