TfKmeans - mlxtend

Clustering falls into the category of unsupervised learning, a subfield of machine learning where the ground truth labels are not available to us in real-world applications. In clustering, our goal is to group samples by similarity (in k-means: Euclidean distance). Cluster re-assignment stops automatically when the algorithm converged. The cluster assignments stored as a Python dictionary; the dictionary keys denote the cluster indeces and the items are Python lists of the sample indices that were assigned to each cluster. Training vectors, where n_samples is the number of samples and n_features is the number of features.