There are many complex, well structured problems, where a hierarchical structure within the data is present. This means, that one or several components of the structure are determined at a higher level by a sub-tree of other components. The concept of fuzzy signatures was introduced to help model these kinds of problems. The data set belonging to a problem has an arbitrary structure, but due to some missing components, the structures of the data may slightly differ. So that these data can be evaluated, aggregation operators are given for each node in the arbitrary structure for the purpose of modifying the structure. To deduce a conclusion for an observation from a data set having the structure mentioned above, fuzzy signature based rule bases and the generalisation of Mamdani-type inference were introduced. In this paper the formerly introduced idea of Mamdani-type inference in fuzzy signature based rule bases will be used for selecting records from an available data base which maximally match the requirements specified in a pattern. Finally a possible application on a realistic example with missing data components will be shown. © 2008 IEEE.

The concept of fuzzy signatures might be useful when modeling complex, well structured problems, where one or several components of the structure are determined at a higher level by a sub-tree of other components. The data set belonging to the problem has an arbitrary structure, from which the structure of the data may slightly differ. An aggregation operator is given for each node, for the purpose of modifying the structure, so that data with missing components can be evaluated. Deducing a conclusion from an observation having such a structure is a key issue. In this paper fuzzy signature based rule bases will be introduced, then the generalisation of the well known Mamdani method for signature based rules will be shown step-by-step. Finally, an example of inference on fuzzy signatures will be discussed.