Fuzzy principal component analysis for fuzzy data

Yoshiyuki Yabuuchi, Junzo Watada, Yoshiteru Nakamori

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

10 Citations (Scopus)

Abstract

In this paper, a fuzzy concept is employed to construct a principal component model which can deal with fuzziness, vagueness or possibility, which is named a fuzzy principal component analysis for fuzzy data. The fuzzy principal component analysis is to analyze a possibility if fuzzy data. The fuzzy principal component analysis for fuzzy data has three formulations according the portions which the possibilities included in fuzzy data are embodied: 1) an eigenvalue, 2) an eigenvector and 3) both eigenvalue and eigenvector. In this paper, we discuss about only the first formulation that an eigenvalue is employed to deal with fuzziness of data. The principal component analysis for fuzzy data is employed in this paper to analyze the features of information technology industry. In this analysis, the financial ratio is employed as indices. And we evaluate the possibility of a company activity in information technology industry.

Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages1127-1132
Number of pages6
Volume2
Publication statusPublished - 1997
Externally publishedYes
EventProceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3) - Barcelona, Spain
Duration: 1997 Jul 11997 Jul 5

Other

OtherProceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3)
CityBarcelona, Spain
Period97/7/197/7/5

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Yabuuchi, Y., Watada, J., & Nakamori, Y. (1997). Fuzzy principal component analysis for fuzzy data. In IEEE International Conference on Fuzzy Systems (Vol. 2, pp. 1127-1132). IEEE.