A multi-objective genetic algorithm approach to rule mining for affective product design

K. Y. Fung, Chun Kit Kwong, Kin Wai Michael Siu, Kai Ming Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

52 Citations (Scopus)


A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.
Original languageEnglish
Pages (from-to)7411-7419
Number of pages9
JournalExpert Systems with Applications
Issue number8
Publication statusPublished - 15 Jun 2012


  • Affective product design
  • Multi-objective genetic algorithm
  • Rule mining

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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