Abstract
We devise a quantitative technique 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. We demonstrate that Euclidean distance based cluster analysis produces meaningful results only when the representative data satisfy Euclidean normalization, as well.
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More From: INFOR: Information Systems and Operational Research
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