Simultaneous Approach to Fuzzy Clustering, Principal Component and Multiple Regression Analysis

• HONDA Katsuhiro

  College of Engineering, Osaka Prefecture University

• YAMAKAWA Asuka

  College of Engineering, Osaka Prefecture University

• ICHIHASHI Hidetomo

  College of Engineering, Osaka Prefecture University

• MIYOSHI Tetsuya

  Faculty of Information Sciences, Hiroshima City University

• OKUYAMA Tetsushi

  Shimadzu Business Systems Corporation

Bibliographic Information

Other Title

• ファジィクラスタリングと回帰と主成分の同時分析法
• ファジィクラスタリング ト カイキ ト シュセイブン ノ ドウジ ブンセキホウ

Abstract

Hathaway and Bezdek's Fuzzy c-Regression Models (FCRM) is regarded as a simultaneous analysis of clustering and multiple regression. In high dimensional setting, the regression techniques do not perform well for reasonable sample sizes because of the inherent sparsity of samples in the cluster. This paper proposes a simultaneous approach to the clustering, principal component analysis and multiple regression analysis. Aiming at knowledge discovery from POS data that are recorded automatically into the cash register transactions, we analyze the relationship between meteorological information and sales of perishables.

Journal

• Transactions of the Institute of Systems, Control and Information Engineers

  Transactions of the Institute of Systems, Control and Information Engineers 13 (5), 236-243, 2000

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Keywords

• fuzzy clustering
• principal component analysis
• multiple regression analysis

Details

• CRID: 1390001205165195392
• NII Article ID: 10004473075
• NII Article ID: 10007781303
• NII Book ID: AN1013280X
• DOI: 10.5687/iscie.13.5_236
• ISSN: 2185811X
• ISSN: 13425668
• NDL BIB ID: 5334950
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I5334950
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed