Meta-k: Towards Unsupervised Prediction of Number of Clusters

Azade Farshad, Samin Hamidi, Nassir Navab

Keywords: Clustering, Self-supervised learning, Meta-learning

Abstract: Data clustering is a well-known unsupervised learning approach. Despite the recent advances in clustering using deep neural networks, determining the number of clusters without any information about the given dataset remains an existing problem. There have been classical approaches based on data statistics that require the manual analysis of a data scientist to calculate the probable number of clusters in a dataset. In this work, we propose a new method for unsupervised prediction of the number of clusters in a dataset given only the data without any labels. We evaluate our method extensively on randomly generated datasets using the scikit-learn package and multiple computer vision datasets and show that our method is able to determine the number of classes in a dataset effectively without any supervision.

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One-sentence Summary: Our work is an attempt to self-supervised prediction of number of clusters in a given data using policy gradient optimization.

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