Clustering is a form of unsupervised classification that aims at grouping data points based on similarity. In this paper, we propose a new partitional clustering algorithm based on the notion of 'contribution of a data point'. We apply the algorithm to content-based image retrieval and compare its performance with that of the k-means clustering algorithm. Unlike the k-means algorithm, our algorithm optimizes on both intra-cluster and inter-cluster similarity measures. It has three passes and each pass has the same time complexity as an iteration in the k-means algorithm. Our experiments on a bench mark image data set reveal that our algorithm improves on the recall at the cost of precision.