K-Means algorithm is one of the most used clustering algorithm for Knowledge Discovery in Data Mining. Seedbased
K-Means is the integration of a small set of labeled data (called seeds) to the K-Means algorithm to improve its
performances and overcome its sensitivity to initial centers. These centers are, most of the time, generated at random or
they are assumed to be available for each cluster. This paper introduces a new efficient algorithm for active seeds selection
which relies on a Min-Max approach that favors the coverage of the whole dataset. Experiments conducted on artificial and real
datasets show that, using our active seeds selection algorithm, each cluster contains at least one seed after a very small
number of queries and thus helps reducing the number of iterations until convergence which is crucial in many KDD applications.

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