main loop

for i in range(self.max_iter):
            #Labels for this iteration
            cur_labels = []
            for medoid in range(0,self.k):
                #Dissimilarity cost of the current cluster
                self.medoids_cost[medoid] = 0
                for k in range(len(X)):
                    #Distances from a data point to each of the medoids
                    d_list = []                    
                    for j in range(0,self.k):
                        d_list.append(euclideanDistance(self.medoids[j], X[k]))
                    #Data points' label is the medoid which has minimal distance to it
                    cur_labels.append(d_list.index(min(d_list)))
                    
                    self.medoids_cost[medoid] += min(d_list)