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Trend Mining: A Visualization Technique to Discover Variable Trends in the Objective Space
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Simulation-Based Optimization)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Simulation-Based Optimization)ORCID iD: 0000-0003-0111-1776
2019 (English)In: Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings / [ed] Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed, Cham, Switzerland: Springer, 2019, Vol. 11411, p. 605-617Conference paper, Published paper (Refereed)
Abstract [en]

Practical multi-objective optimization problems often involve several decision variables that influence the objective space in different ways. All variables may not be equally important in determining the trade-offs of the problem. Decision makers, who are usually only concerned with the objective space, have a hard time identifying such important variables and understanding how the variables impact their decisions and vice versa. Several graphical methods exist in the MCDM literature that can aid decision makers in visualizing and navigating high-dimensional objective spaces. However, visualization methods that can specifically reveal the relationship between decision and objective space have not been developed so far. We address this issue through a novel visualization technique called trend mining that enables a decision maker to quickly comprehend the effect of variables on the structure of the objective space and easily discover interesting variable trends. The method uses moving averages with different windows to calculate an interestingness score for each variable along predefined reference directions. These scores are presented to the user in the form of an interactive heatmap. We demonstrate the working of the method and its usefulness through a benchmark and two engineering problems.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2019. Vol. 11411, p. 605-617
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11411
Keywords [en]
Visualization, Data mining, Multi-criteria decision making, Decision space, Trend analysis, Objective space
National Category
Other Computer and Information Science
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-16712DOI: 10.1007/978-3-030-12598-1_48Scopus ID: 2-s2.0-85063032277ISBN: 978-3-030-12597-4 (print)ISBN: 978-3-030-12598-1 (electronic)OAI: oai:DiVA.org:his-16712DiVA, id: diva2:1298632
Conference
10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019
Projects
Knowledge-Driven Decision Support (KDDS)
Funder
Knowledge Foundation, 41231
Note

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 11411)

Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-05-23Bibliographically approved

Open Access in DiVA

The full text will be freely available from 2020-02-03 00:00
Available from 2020-02-03 00:00

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Publisher's full textScopus

Authority records BETA

Bandaru, SunithNg, Amos H. C.

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