An empirical comparison between stochastic and deterministic centroid initialisation for K-Means variations

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 due to only being able to find local minima, the positions of the initial clustering centres (centroids) can greatly affect the clustering solution. Over the years many K-Means variations and initialisation 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 on average achieve better performance than stochastic methods. However, there is a trade-off: stochastic methods executed multiple times can result to better clustering. Nevertheless, factoring in execution time deterministic methods can be competitive and result in a good clustering solution. These conclusions are obtained through extensive benchmarking using different data set model generators from various studies as well as standalone clustering and real-world data sets.