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Although K-means clustering algorithm is simple and popular, it has a fundamental drawback of falling into local optima that depend on the randomly generated initial centroid values. Optimization algorithms are well known for their ability to guide iterative computation in searching for global optima. They also speed up the clustering process by achieving early convergence. Contemporary optimization...
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