Shot clustering is an important aspect of video content analysis. The basic task of shot clustering is to classify shots based on their low-level features. This paper describes a novel shot-clustering technique. Beginning with an initial classification of the shot set, our algorithm proceeds with merging and splitting iteration alternatively to reduce the errors in the initial results. The main advantage of this algorithm is that it does not need any experiential parameters or thresholds, nor does it need any manual interaction. In this way, our algorithm overcomes shortcomings of tradi tional clustering algorithm and works well in practical systems.