We also employed the well-known Euclidean -means algorithm and three variations of it using non-metric distance measures. The -means algorithm is an iterative algorithm to minimize the least squares error criterion [DH73]. A cluster is represented by its center , the mean of all samples in . The centers are initialized with a random selection of data objects. Each sample is then labeled with the index of the nearest or most similar center. In classical -means, `nearest' means the point with the smallest Euclidean distance. However, -means can be generalized by substituting nearness with any other notion of similarity. For our comparison, we will use all four definitions of the similarity between two objects and as introduced in the previous section. Subsequent re-computing of the mean for each cluster and re-assigning the cluster labels is iterated until convergence to a fixed labeling after iterations. The complexity of this algorithm is .