A cutting plane algorithm for globally solving low dimensional k-means clustering problems

arXiv:2402.13595v1 Announce Type: cross
Abstract: Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods. There is a variety of approximate algorithms for the k-means problem, but computing the globally optimal solution is in general NP-hard. In this paper we consider the k-means problem for instances with low dimensional data and formulate it as a structured concave assignment problem. This allows us to exploit the …

abstract algorithm algorithms arxiv clustering computing cs.lg data data science k-means low machine machine learning math.oc plane science solution stat.ml tools type