The interpretation of fuzzy classification output varies with the method used. Interpreting fuzzy classification wombling output is similar to interpreting wombling tables and maps for any other data set.
Confusion index (CI) and classification entropy (CE) output are similar to each other. Remember that the confusion index and classification entropy represent the degree of fuzziness in the data (as explained in Detecting boundaries on fuzzy classes). Locations with CI or CE values close to one have membership dispersed between classes, while those with lower CI or CE values have more distinct class membership.
After fuzzy classification using the CI or CE method, BoundarySeer produces two new map layers, a representation of the newly-created fuzzy class data set and a boundary layer illustrating the CI or CE values.
For polygon and raster data, the boundary layer is the same type as the data. For point data, however, the boundary layer is a set of polygons: the Voronoi polygons. Voronoi polygons describe proximity relationships. The edges of Voronoi polygons are equidistant between neighboring points, they delimit areas closer to the enclosed point than any other point in the data set.
These polygons are colored by the CI or CE value, with darker polygons indicating higher CI or CE values, that is, more fuzziness in the data. Darker locations are more transitional, less distinct, and therefore more boundary-like than lighter areas with lower CI or CE values.
Next step: You may wish to repeat the fuzzy classification with different parameters (k, epsilon, and phi) to see the effect of these parameters on the outcome.
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