How to create fuzzy classes

Go to "Detect Boundary" on the "Data" menu, or right click on the data set you wish to classify in the project window and choose "Detect Boundary." Select "Fuzzy classification."

The fuzzy classification dialog consists of four tabs. To create classes, you will just need to complete the first two tabs. Once you have fuzzy classes, you may detect boundaries on it.

To detect boundaries using wombling, classification entropy or confusion index directly when you classify the data, go to How to detect boundaries on fuzzy classes instead.

To detect boundaries with spatially constrained clustering or wombling with location uncertainty, get fuzzy classes and then follow instructions for these procedures using the fuzzy class data set.

Steps

  1. "General" tab

    1. Select the data set to classify from the pull down list.

    2. BoundarySeer will produce a new data set of the spatial locations with their fuzzy class memberships. You can name the data set or accept the default, note that the default name contains the word "Class".

    3. There will be a place to specify a name for the new boundary, but as you won't create a new boundary this feature does not apply.

    4. Select the number of classes (k).

    5. Select whether to perform the analysis on one variable, the entire data set, or another variable set.

    6. The default is to standardize the variables before analysis. Clear this option if you decide not to standardize.

  2. "Method" tab

    1. Select a fuzziness exponent (phi or f).

    2. Select a stopping criterion (epsilon or e).

    3. Clear the "Detect boundaries using:" checkbox.

  3. BoundarySeer will create a new data set in the project of the fuzzy classes. You may then use the boundary detection method of your choice on the fuzzy class data set.

See also: