2 NONPARAMETRIC MODELS FOR FUSING HETEROGENEOUS FUZZY DATA

Two models are discussed that integrate heterogeneous fuzzy data of th ree types: real numbers, real intervals, and real fuzzy sets. The arch itecture comprises three modules: 1) an encoder that converts the mixe d data into a uniform internal representation; 2) a numerical processi ng core that uses the internal representation to solve a specified tas k; and 3) a decoder that transforms the internal representation back t o an interpretable output format. The core used in this study is fuzzy clustering, but there are many other operations that are facilitated by the models. Two schemes for encoding the data and decoding it after clustering are presented. One method uses possibility and necessity m easures for encoding and several variants of a center of gravity defuz zification method for decoding. The second approach uses piecewise lin ear splines to encode the data and decode the clustering results, Both procedures are illustrated using two small sets of heterogeneous fuzz y data.