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Quantifying similarity for handling information in knowledge bases
Author(s) -
Bandemer Hans
Publication year - 1990
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180040206
Subject(s) - similarity (geometry) , knowledge base , character (mathematics) , set (abstract data type) , computer science , data mining , base (topology) , fuzzy set , artificial intelligence , fuzzy logic , natural language processing , mathematics , information retrieval , mathematical analysis , geometry , image (mathematics) , programming language
When constructing diagnostic systems or using knowledge‐based systems, e.g. in analytical chemistry, features of different type and character, represented by numbers, trajectories or linguistic variables such as intensities or colours, must be considered. To find neighbourhoods or to fill in missing values, the notion of similarity is of essential importance. The paper presents a new fuzzy‐set‐theory‐based approach to quantifying similarity and provides a system of rules to be implemented into the diagnostic part of the knowledge base to be used.