Using the analytic hierarchy process for quantifying and classifying objects with multiple attributes

A quantitative technique is devised for grouping objects with multiple attributes by extending a recently developed methodology in which the Analytic Hierarchy Process has been formulated in a Euclidean vector space. The idea is illustrated in the context of two practical application scenarios involving group technology and plant biotechnology research. It is demonstrated that Euclidean distance based cluster analysis produces meaningful results only when the representative data satisfy Euclidean normalization, as well.