An empirical comparison between stochastic and deterministic centroid initialisation for K-Means variations
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K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking. Despite its wide application it is well-known that it suffers from a series of disadvantages, such as the positions of the initial clustering centres (centroids), which can greatly affect the clustering solution. Over the years many K-Means variations and initialisations techniques have been proposed with different degrees of complexity. In this study we focus on common K-Means variations and deterministic initialisation techniques and we first show that more sophisticated initialisation methods reduce or alleviates the need of complex K-Means clustering, and secondly, that deterministic methods can achieve equivalent or better performance than stochastic methods. These conclusions are obtained through extensive benchmarking using different model data sets from various studies as well as clustering data sets.
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