I need to be able to select first N most representative points from each cluster calculated by K-means. To do so, I am aiming to calulate the distance of each point to its own cluster center and take the ones with the smallest values. To do this, I have used the K-means transform function:
kmeans= KMeans(n_clusters=18,random_state=2, n_init = 50, max_iter=500) clusterer= kmeans.fit(data) distances= kmeans.transform(data)[:,0]
To validate the results, I have calculated the points closest to the cluster centers:
closest_org, _ = pairwise_distances_argmin_min(clusterer.cluster_centers_, data)
and the points that I have calculated as the closest ones are not the one that have a smallest distance. Could you please advise on the optimal and correct way to calculate get the N closest points from each cluster?