Mining for creativity: Determining the creativity of ideas through data mining techniques

Christine A. Toh, Elizabeth M. Starkey, Conrad S. Tucker, Scarlett R. Miller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The emergence of ideation methods that generate large volumes of early-phase ideas has led to a need for reliable and efficient metrics for measuring the creativity of these ideas. However, existing methods of human judgment-based creativity assessments, as well as numeric model-based creativity assessment approaches suffer from low reliability and prohibitive computational burdens on human raters due to the high level of human input needed to calculate creativity scores. In addition, there is a need for an efficient method of computing the creativity of large sets of design ideas typically generated during the design process. This paper focuses on developing and empirically testing a machine learning approach for computing design creativity of large sets of design ideas to increase the efficiency and reliability of creativity evaluation methods in design research. The results of this study show that machine learning techniques can predict creativity of ideas with relatively high accuracy and sensitivity. These findings show that machine learning has the potential to be used for rating the creativity of ideas generated based on their descriptions.

Original languageEnglish (US)
Title of host publication29th International Conference on Design Theory and Methodology
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791858219
DOIs
StatePublished - Jan 1 2017
EventASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017 - Cleveland, United States
Duration: Aug 6 2017Aug 9 2017

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume7

Other

OtherASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017
CountryUnited States
CityCleveland
Period8/6/178/9/17

Keywords

  • Computation
  • Creativity assessment
  • Design creativity
  • Machine learning

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

Fingerprint Dive into the research topics of 'Mining for creativity: Determining the creativity of ideas through data mining techniques'. Together they form a unique fingerprint.

  • Cite this

    Toh, C. A., Starkey, E. M., Tucker, C. S., & Miller, S. R. (2017). Mining for creativity: Determining the creativity of ideas through data mining techniques. In 29th International Conference on Design Theory and Methodology (Proceedings of the ASME Design Engineering Technical Conference; Vol. 7). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2017-68304