Global k-means++: an effective relaxation of the global k-means clustering algorithm
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The k-means algorithm is a very prevalent clustering method because of its simplicity, effectiveness, and speed, but its main disadvantage is its high sensitivity to the initial positions of the cluster centers. The global k-means is a deterministic algorithm proposed to tackle the random initialization problem of k-means but requires high computational cost. It partitions the data to K clusters by solving all k-means sub-problems incrementally for k=1,…, K. For each k cluster problem, the method executes the k-means algorithm N times, where N is the number of data points. In this paper, we propose the global k-means++ clustering algorithm, which is an effective way of acquiring quality clustering solutions akin to those of global k-means with a reduced computational load. This is achieved by exploiting the center section probability that is used in the effective k-means++ algorithm. The proposed method has been tested and compared in various well-known real and synthetic datasets yielding very satisfactory results in terms of clustering quality and execution speed.
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