FCM Classifier for High Dimensional Data

• Ichihashi Hidetomo

  Osaka Prefecture University

• Honda katsuhiro

  Osaka Prefecture University

• Notsu Akira

  Osaka Prefecture University

• Hattori Takao

  Osaka Prefecture University

• Miyamoto Eri

  Osaka Prefecture University

Bibliographic Information

Other Title

• 高次元データのためのFCM識別器

Abstract

A fuzzy classifier based on fuzzy c-means (FCM) clustering has shown a decisive generalization ability in classification. The FCM classifier uses covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high dimensional data. This paper proposes a way of directly handling high dimensional data in FCM clustering and classification. The proposed classifier outperforms well established relational classifier known as k-nearest neighbor (k-NN) on the benchmark set of COREL image collection, which was used by James Wang for tests of his Simplicity System.

Journal

• Proceedings of the Fuzzy System Symposium

  Proceedings of the Fuzzy System Symposium 23 (0), 541-541, 2007

  Japan Society for Fuzzy Theory and Intelligent Informatics

Keywords

• Fuzzy Clustering
• High Dimensional Data
• Image Classification

Details

• CRID: 1390282680643612288
• NII Article ID: 130004730297
• DOI: 10.14864/fss.23.0.541.0
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed