Chapter 6: Handling Nonnumerical Variables

Chapter 6: Handling Nonnumerical Variables

Overview

Given the representative sample, as described in Chapter 5 , it may well consist of a mixture of variable types. Nonnumerical, or alpha , variables present a set of problems different from those of numerical variables. Chapter 2 briefly examined the different types of nonnumerical variables, where they were referred to as nominal and categorical. Distinguishing between the different types of alpha variables is not easy, as they blend into a continuum. Whenever methods of handling alpha variables are used, they must be effective at handling all types across the continuum.

The types of problems that have to be handled depend to some extent on the capabilities, and the needs, of the modeling tools involved. Some tools, such as decision trees, can handle alpha values in their alpha form. Other tools, such as neural networks, can handle only a numeric representation of the alpha value. The miner may need to use several different modeling tools on a data set, each tool having different capabilities and needs. Whatever techniques are used to prepare the data set, they should not distort its information content (i.e., add bias). Ideally, the data prepared for one tool should be useable by any other tool?and should give materially the same results with any tool that can handle the data.

Since all tools can handle numerical data but some tools cannot handle alpha data, the miner needs a method of transforming alpha values into appropriate numerical values.

Chapter 2

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Chapter 6: Handling Nonnumerical Variables

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