Defines a quantile classification method.


The Quantile coclass creates an equal (or close to equal) number of values in each class. For example, if there were 12 values, then three classes would represent four values each.

This classification is particularly effective for ranked values. A company can measure sales performance of business locations and draw the respective businesses in their rank of sales performance. This classification yields visually attractive maps because all of the classes have the same number of features.

However, this classification might obscure the natural distribution of values; clusters of values may be split or combined with other values. This classification is best applied to values that have a linear distribution. If you have an even number of classes, the value delimiting the middle classes is the same as the median of statistical sampling.

Because features are grouped by the number in each class, the resulting map can be misleading. Similar features can be placed in adjacent classes, or features with widely different values can be put in the same class. You can minimize this distortion by increasing the number of classes.

Supported Platforms

Windows, Solaris, Linux


Interfaces Description
IClassify Provides access to members that control the classification methods.
IClassifyGEN Provides access to members that control classification.

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