In this paper we present a method for the unsupervised clustering of
high-dimensional binary data, with a special focus on electronic healthcare
records. We present a robust and efficient heuristic to face this problem using
tensor decomposition. We present the reasons why this approach is preferable
for tasks such as clustering patient records, to more commonly used
distance-based methods. We run the algorithm on two datasets of healthcare
records, obtaining clinically meaningful results.Comment: Presented at 2017 Machine Learning for Healthcare Conference (MLHC
2017). Boston, M
