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Ottar Ness,Sheila McNamee,?yvind Kvellofor clustering, which has shown to be more computationally efficient than systematic (i.e., repetitive) approaches when the number of clusters in a data set is unknown. Illustrative experiments showing the influence of local optimization on the efficiency of the evolutionary search are also presente

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were also encouraged. The Volume is divided into 2 parts: Part-I: Function Approximation and Classification – Theoretical Foundations Part-II: Function Approximation and Classification – Success Stories and Real World Applications Part I on Function Approximation and Classification – Theoretical Fo