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The Value of Information in Clustering Dense Matrices: When and How to Make Use of Information

Published online by Cambridge University Press:  26 May 2022

F. Endress*
Affiliation:
Department of Engineering, University of Cambridge, United Kingdom TUM School of Engineering and Design, Technical University of Munich, Germany
T. Kipouros
Affiliation:
Department of Engineering, University of Cambridge, United Kingdom
T. Buker
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
S. Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
P. J. Clarkson
Affiliation:
Department of Engineering, University of Cambridge, United Kingdom

Abstract

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Characterising a socio-technical system by its underlying structure is often achieved by cluster analyses and bears potentials for engineering design management. Yet, highly connected systems lack clarity when systematically searching for structures. At two stages in a clustering procedure (pre-processing and post-processing) modelled and external information were used to reduce ambiguity and uncertainty of clustering results. A holistic decision making on 1) which information, 2) when, and 3) how to use is discussed and considered inevitable to reliably cluster highly connected systems.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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