liste = [ (1, [“A”,1,”C”]) , (3, [“A”,1,”C”]) , (1000.256, [“B”,1,”C”]) , (1002, [“C”,1,”C”]) , (5, [“D”,1,”C”]) , (999.3, [“E”,1,”C”]) , (2.5, [“F”,1,”C”])] xxx=np.array(liste,dtype=object) best_n_clusters,best_silhouette_score=None,-1 range_n_clusters=[2,3,4,5,6,7,8,9,10,11] for n_clusters in range_n_clusters: clusterer=KMeans(n_clusters=n_clusters) cluster_labels=clusterer.fit(xxx) silhouette_avg=silhouette_score(xxx,cluster_labels) if silhouette_avg>best_silhouette_score: best_silhouettescore=silhouette_avg best_n_clusters=n_clusters kmeans=KMeans(n_clusters=best_n_clusters) cluster_labels=kmeans.fit(xxx) for r in range(best_n_clusters): group=xxx[cluster_labels==i] print(f”Groupe {r+1} : {group}”) I have an initial list, composed of […]