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Research Papers: Design Automation

Evaluating Clustering Algorithms for Identifying Design Subproblems

[+] Author and Article Information
Jeffrey W. Herrmann

Department of Mechanical Engineering and
Institute for Systems Research,
University of Maryland,
College Park, MD 20742

Michael Morency

Institute for Systems Research,
University of Maryland,
College Park, MD 20742

Azrah Anparasan, Erica L. Gralla

Engineering Management and
Systems Engineering,
George Washington University,
Washington, DC 20052

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received December 7, 2017; final manuscript received April 18, 2018; published online May 23, 2018. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 140(8), 081401 (May 23, 2018) (12 pages) Paper No: MD-17-1814; doi: 10.1115/1.4040176 History: Received December 07, 2017; Revised April 18, 2018

Understanding how humans decompose design problems will yield insights that can be applied to develop better support for human designers. However, there are few established methods for identifying the decompositions that human designers use. This paper discusses a method for identifying subproblems by analyzing when design variables were discussed concurrently by human designers. Four clustering techniques for grouping design variables were tested on a range of synthetic datasets designed to resemble data collected from design teams, and the accuracy of the clusters created by each algorithm was evaluated. A spectral clustering method was accurate for most problems and generally performed better than hierarchical (with Euclidean distance metric), Markov, or association rule clustering methods. The method's success should enable researchers to gain new insights into how human designers decompose complex design problems.

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Figures

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Fig. 3

Experiment A results for the Markov clustering algorithm

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Fig. 4

Experiment A results for the association rules clustering algorithm

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Fig. 1

Experiment A results for the hierarchical clustering algorithm

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Fig. 2

Experiment A results for the spectral clustering algorithm: accuracy by threshold value (left) and by k for the best threshold value (right), both at 10% noise

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Fig. 5

Experiment B results for problem sets with 35 variables. Each problem set is denoted by values for NUMVAR, LENGTH, and SIZE (μ,σ), with A = (4,2.5), B = (10,6.25), C = (4, 5), followed by the noise level.

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Fig. 6

Experiment C results for four algorithms. Each problem set is denoted by levels of overlap and noise.

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