Local algorithms for interactive clustering
This work addresses the need for efficient and adaptable clustering methods in applications requiring interactive and local adjustments, though it appears incremental as it builds on existing stability assumptions.
The paper tackles the problem of interactive clustering under stability assumptions by developing algorithms that start from any initial clustering and make only local changes, resulting in provably efficient and accurate clusterings, with demonstrated good performance on real-world data.
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.