Local algorithms for interactive clustering
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):550-558, 2014.
We study the design of interactive clustering algorithms for data sets satisfying natural stability assumptions. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable features in many applications. We show that in this constrained setting one can still design provably efficient algorithms that produce accurate clusterings. We also show that our algorithms perform well on real-world data.