Summary
Clustering techniques for the generation of fuzzy models have been used and have shown promising results in many applications involving complex data. This chapter proposes a new incremental clustering technique to improve the discovery of local structures in the obtained fuzzy models. This clustering method is evaluated on two data sets and the results are compared with the results of other clustering methods. The proposed clustering approach is applied for nonlinear Takagi–Sugeno (TS) fuzzy modeling. This incremental clustering procedure that generates clusters that are used to form the fuzzy rule antecedent part in online mode is used as a first stage of the learning process.
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Martínez, B., Herrera, F., Fernández, J., Marichal, E. (2008). An Incremental Clustering Method and Its Application in Online Fuzzy Modeling. In: Bello, R., Falcón, R., Pedrycz, W., Kacprzyk, J. (eds) Granular Computing: At the Junction of Rough Sets and Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76973-6_11
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DOI: https://doi.org/10.1007/978-3-540-76973-6_11
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