Powered Outer Probabilistic Clustering

Clustering is one of the most important concepts for unsupervised learning in machine learning. While there are numerous clustering algorithms already, many, including the popular one k-means algorithm, require the number of clusters to be specified in advance, a huge drawback. Some studies use the silhouette coefficient to determine the optimal number of clusters. In this study, we introduce a novel algorithm called Powered Outer Probabilistic Clustering, show how it works through back-propagation (starting with many clusters and ending with an optimal number of clusters), and show that the algorithm converges to the expected (optimal) number of clusters on theoretical examples.