Concept Identification for Complex Engineering Datasets
- URL: http://arxiv.org/abs/2206.06155v1
- Date: Thu, 9 Jun 2022 09:39:46 GMT
- Title: Concept Identification for Complex Engineering Datasets
- Authors: Felix Lanfermann and Sebastian Schmitt
- Abstract summary: A novel concept quality measure is proposed, which provides an objective value for a given definition of concepts in a dataset.
It is demonstrated how these concepts can be used to select archetypal representatives of the dataset which exhibit characteristic features of each concept.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Finding meaningful concepts in engineering application datasets which allow
for a sensible grouping of designs is very helpful in many contexts. It allows
for determining different groups of designs with similar properties and
provides useful knowledge in the engineering decision making process. Also, it
opens the route for further refinements of specific design candidates which
exhibit certain characteristic features. In this work, an approach to define
meaningful and consistent concepts in an existing engineering dataset is
presented. The designs in the dataset are characterized by a multitude of
features such as design parameters, geometrical properties or performance
values of the design for various boundary conditions. In the proposed approach
the complete feature set is partitioned into several subsets called description
spaces. The definition of the concepts respects this partitioning which leads
to several desired properties of the identified concepts, which cannot be
achieved with state-of-the-art clustering or concept identification approaches.
A novel concept quality measure is proposed, which provides an objective value
for a given definition of concepts in a dataset. The usefulness of the measure
is demonstrated by considering a realistic engineering dataset consisting of
about 2500 airfoil profiles where the performance values (lift and drag) for
three different operating conditions were obtained by a computational fluid
dynamics simulation. A numerical optimization procedure is employed which
maximizes the concept quality measure, and finds meaningful concepts for
different setups of the description spaces while also incorporating user
preference. It is demonstrated how these concepts can be used to select
archetypal representatives of the dataset which exhibit characteristic features
of each concept.
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