Clustering for Binary Featured Datasets

Code

• github.com/peta78/Powered-Outer-Probabilistic-Clustering
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Abstract

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.

Tasks

Reproductions