Given a weighted and complete graph G = (V, E), V denotes the set of n objects to be clustered, and the weight d(u, v) associated with an edge (u, v) belonging to E denotes the dissimilarity between objects u and v. The diameter of a cluster is the maximum dissimilarity between pairs of objects in the cluster, and the split of a cluster is the minimum dissimilarity between objects within the cluster and objects outside the cluster. In this paper, we propose a new criterion for measuring the goodness of clusters: the ratio of the minimum split to the maximum diameter, and the objective is to maximize the ratio. For k = 2, we present an exact algorithm. For k >= 3, we prove that the problem is NP-hard and present a factor of 2 approximation algorithm on the precondition that the weights associated with E satisfy the triangle inequality. The worst-case runtime of both algorithms is O(n^3). We compare the proposed algorithms with the Normalized Cut by applying them to image segmentation. The experimental results on both natural and synthetic images demonstrate the effectiveness of the proposed algorithms.