Georgia Institute of Technology College of Sciences

There has been substantial work on approximation algorithms for clustering data under distance-based objective functions such as k-median, k-means, and min-sum objectives. This work is fueled in part by the hope that approximating these objectives well will indeed yield more accurate solutions. That is, for problems such as clustering proteins by function, or clustering images by subject, there is some unknown correct "target" clustering and the implicit assumption is that clusterings that are approximately optimal in terms of these distance-based measures are also approximately correct in terms of error with respect to the target. In this work we show that if we make this implicit assumption explicit -- that is, if we assume that any c-approximation to the given clustering objective Phi is epsilon-close to the target -- then we can produce clusterings that are O(epsilon)-close to the target, even for values c for which obtaining a c-approximation is NP-hard. In particular, for the k-median, k-means, and min-sum objectives, we show that we can achieve this guarantee for any constant c > 1. Our results show how by explicitly considering the alignment between the objective function used and the true underlying clustering goals, one can bypass computational barriers and perform as if these objectives were computationally substantially easier. This talk is based on joint work with Avrim Blum and Anupam Gupta (SODA 2009), Mark Braverman (COLT 2009), and Heiko Roeglin and Shang-Hua Teng (ALT 2009).