Master thesis: Meta-clustering

Thesis by: Samin Hamidi
Advisor: Azade Farshad
Supervision by: Prof. Dr. Nassir Navab
Due date:

Abstract

Clustering is a well-known unsupervised learning approach where the data is partitioned into multiple clusters for assignment of pseudo-labels. The number of clusters in data without labels is an unknown parameter that is usually manually set or is calculated by a data scientist using classical approaches. With recent advances in machine learning, these approaches face limitations, thus the need of automatic prediction of number of clusters emerges to move towards a better learning with less supervision. In this thesis we focus on predicting the number of clusters in the k-means clustering method using meta-learning or learning to learn.

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