L1-Space Based Models for Clustering and Regression: Fuzzy Clustering and Mixture Densities

  • Sadaaki Miyamoto
  • Takatsugu Koga
  • Yoichi Nakayama
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 125)

Abstract

Fuzzy clustering and mixture densities based on L 1-space are studied and application to regression models are considered. In the mixture density model, the EM algorithm is used for estimating the model parameters. A fast algorithm for calculating cluster centers in the alternative optimization of fuzzy c-means is derived. The same procedure can be used for estimating the means in the EM alorithm. Regression models with clustering based on absolute deviations are discussed. Linear programming algorithms are used for estimating the regression model parameters.

Keywords

Absolute Deviation Gaussian Mixture Model Fuzzy Model Fuzzy Cluster Mixture Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Sadaaki Miyamoto
    • 1
  • Takatsugu Koga
    • 2
  • Yoichi Nakayama
    • 2
  1. 1.Institute of Engineering Mechanics and SystemsUniversity of TsukubaIbarakiJapan
  2. 2.Graduate School of Systems and Information EngineeringUniversity of TsukubaIbarakiJapan

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