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

Conference paper (2022)

Authors

F. Endress University of Cambridge, Technische Universität München
Timoleon Kipouros University of Cambridge
T. Buker Friedrich-Alexander-Universität Erlangen-Nürnberg
S. Wartzack Friedrich-Alexander-Universität Erlangen-Nürnberg
P.J. Clarkson University of Cambridge
Research Group
Applied Ergonomics and Design (IDE) (TU Delft)
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Published Date

2022

Language

English

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Faculty

Industrial Design Engineering

Department

Human-Centered Design

Research Group

Applied Ergonomics and Design

Abstract

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.