Constructing Fuzzy Graphs from Examples

Berthold, Michael R.; Huber, Klaus-Peter

Constructing Fuzzy Graphs from Examples

Files

BeHu99_fuzzygraph_idaij.pdf(225.48 KB)

Date

1999

Authors

Berthold, Michael R.

Huber, Klaus-Peter

Publication type

Journal article

Published in

Intelligent Data Analysis ; 3 (1999), 1. - pp. 37-53

Abstract

Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an efficient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs offer quick insights into the structure of the example data, that is, the underlying model. The underlying algorithm is demonstrated using several Java applets, which can be found under "Electronic annexes" on www.elsevier.com/locate/ida.

Subject (DDC)

004 Computer Science

Keywords

Fuzzy Graphs,Learning,Rule Extraction,Function Approximation,Interpretation

Cite This

ISO 690

BERTHOLD, Michael R., Klaus-Peter HUBER, 1999. Constructing Fuzzy Graphs from Examples. In: Intelligent Data Analysis. 3(1), pp. 37-53. Available under: doi: 10.1016/S1088-467X(99)00004-9

BibTex

@article{Berthold1999Const-5413,
  year={1999},
  doi={10.1016/S1088-467X(99)00004-9},
  title={Constructing Fuzzy Graphs from Examples},
  number={1},
  volume={3},
  journal={Intelligent Data Analysis},
  pages={37--53},
  author={Berthold, Michael R. and Huber, Klaus-Peter}
}

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