Clustering is an unsupervised learning tool. Clustering has been studied for decades, with the first and in some cases still popular clustering algorithms emerging over fifty years ago. Nevertheless, new challenges have kept the area active and made it one of the topical and growing fields today. Most prominently, one of these challenges is the rise of big data applications. Standard clustering methods do usually not scale well to scenarios where we assume to have polylogarithmic or even constant amounts of space. But also new aspects as privacy and fairness lead to new and exciting research problems.
We thus study clustering problems under different new aspects, including:
Usually, we aim for approximation algorithms with constant or even arbitrarily good approximation guarantees. However, when developing or analyzing practical algorithms, we are also interested in relaxed statements, e.g. algorithms with a higher approximation guarantee than optimally possible, or algorithms which perform very good under data niceness assumptions.