Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an ecient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs oer quick insights into the structure of the example data, that is, the underlying model. The underlying algorithm is demonstrated using several Java applets, which can be found under ÔElectronic annexesÕ on www.elsevier.com/locate/ida. Ó
For instance, the work in REF presents an efficient algorithm for inducing locally independent fuzzy rules from examples.
Constructing fuzzy graphs from examples
Intell. Data Anal.
Intell. Data Anal.
Computer Science