The k-means procedure classifies a given data set by using a user defined number of clusters, k, a priori. The centroids can be placed randomly, or algorithmically, but it should be noted that the initial placement will affect the result. The next step is to analyze each point within the data set and group it with the nearest centroid according to some distance metric. When all points have been assigned to a group, a new centroid is calculated for each group as a barycenter of the cluster, resulting from the previous step. Once the k new centroids are calculated, the algorithm reiterates through the data set, and each sample is again assigned to a cluster based on its distance to the new centroids. This process is continued until the position of the centroids no longer change.

Sir Martin made clear he intended to “stick to his last” by staying in an industry he knew well and enjoyed, and that he would not wait long before making his next move.

Cissampelina, the intensely sweetish-bitter principle found in this body, is very like calumbin in some physical properties; yet we have been wont to associate other therapeutic ideas with pareira, than with calumba.

He turned back to the scene before him and the enormous new block of council dwellings. The design was some way after Corbusier but the block was built up on plinths and resembled an Atlantic liner swimming diagonally across the site.