Analysing Fuzzy Sets Through Combining Measures of Similarity and
  Distance
  Reasoning with fuzzy sets can be achieved through measures such as similarity
and distance. However, these measures can often give misleading results when
considered independently, for example giving the same value for two different
pairs of fuzzy sets. This is particularly a problem where many fuzzy sets are
generated from real data, and while two different measures may be used to
automatically compare such fuzzy sets, it is difficult to interpret two
different results. This is especially true where a large number of fuzzy sets
are being compared as part of a reasoning system. This paper introduces a
method for combining the results of multiple measures into a single measure for
the purpose of analysing and comparing fuzzy sets. The combined measure
alleviates ambiguous results and aids in the automatic comparison of fuzzy
sets. The properties of the combined measure are given, and demonstrations are
presented with discussions on the advantages over using a single measure.
