Clustering techniques are used to discover structure in data by optimizing a defined criterion function. Most of these methods assume that the data are stationary, and these techniques are based on gradient descent which converge to a locally optimal clustering. There are many potential applications that require clustering to be performed in non-stationary temporal environments. In this paper, we investigate the applicability of a dan-based evolutionary optimization method for clustering data in non-stationary environments. Due to the stochastic nature of the technique, the problem of becoming entrapped in local optima is avoided, and the method can converge to (nearly) optimal clusters. Different cases are considered in the experiments, and the results demonstrate the efficacy of the evolutionary approach for clustering time-varying data. References: 27
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