Publikation: Constructing Fuzzy Graphs from Examples
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1999
Autor:innen
Huber, Klaus-Peter
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Titel in einer weiteren Sprache
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Published
Erschienen in
Intelligent Data Analysis. 1999, 3(1), pp. 37-53. Available under: doi: 10.1016/S1088-467X(99)00004-9
Zusammenfassung
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.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Fuzzy Graphs, Learning, Rule Extraction, Function Approximation, Interpretation
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ISO 690
BERTHOLD, Michael R., Klaus-Peter HUBER, 1999. Constructing Fuzzy Graphs from Examples. In: Intelligent Data Analysis. 1999, 3(1), pp. 37-53. Available under: doi: 10.1016/S1088-467X(99)00004-9BibTex
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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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