Some considerations on data mining from questionnaires by constructing fuzzy signatures based on factor analysis

Laszlo T. Koczy, Ojaras Purvinis, Dalia Susniene

Research output: Contribution to journalArticle


To interpret and to process the answers to questionnaires with large amount of questions may be not easy task. They are multidimensional data, sometimes with high dimensionality (in the hundreds). Therefore, it is necessary that some data reduction approach should be employed. On the other hand, answers to specific questions in questionnaires are imprecise, and the type and degree of imprecision is determined by the kind of the questions. The authors of the paper consider the imprecise answers to management type questions using a numerical scale as fuzzy degrees, and based on the semantic connections among the individual questions, a hierarchical structure is assumed. The paper suggests the use of factor analysis in order to determine this hierarchical structure, and thus the construction of fuzzy signatures from the tree graph representing the connections among the questions and answers, and the values normalized into membership degrees are assigned to the leaves of this tree. An interesting issue is how to determine the aggregations at the intermediate nodes. This may happen based on management science domain expert knowledge, and validated by the obtained results. Kohonen maps are used to demonstrate the clusters emerging among the overall fuzzy degrees representing the Fuzzy Signatures. The evaluation brings some results that partly confirm soft science based assumptions about employee behavior in the literature, and partly bring some interesting novel recognitions that may be brought in feedback to the original management science related problem, where the new method is illustrated.

Original languageEnglish
Pages (from-to)3739-3749
Number of pages11
JournalJournal of Intelligent and Fuzzy Systems
Issue number4
Publication statusPublished - 2019


  • Clustering
  • Factor analysis
  • Fuzzy signature
  • Kohonen maps
  • Questionnaires

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

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