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In this paper, a novel approach for initializing the spherical K-means algorithm is proposed. It is based on calculating well distributed seeds across the input space. Also, a new measure for calculating vectors’ directional variance is formulated, to be used as a measure of clusters’ compactness. The proposed initialization scheme is compared with the classical K-means – where initial seeds are specified randomly or arbitrarily – on two datasets. The assessment was based on three measures: an objective function that measures intra cluster similarity, cluster compactness and time to converge. The proposed algorithm (called initialized K-means) outperforms the classical (random) K-means when intra cluster similarity and cluster compactness were considered for several values of k (number of clusters). As far as convergence time is concerned, the initialized K-means converges faster than the random K-means for …