Evolving maximum likelihood clustering algorithm

This paper proposes an online evolving fuzzy clustering algorithm based on maximum likelihood estimator. In this methodology, the distance from a point to center of the cluster is computed by maximum likelihood similarity of data. The mathematical formulation is developed from the Takagi-Sugeno (TS) fuzzy inference system. The performance and application of the proposed methodology is based on prediction of the Box-Jenkins (Gas Furnace) time series. Computational results of a comparative analysis with other methods widely cited in the literature illustrates the effectiveness of the proposed methodology.