On clusterings-good, bad and spectral
Ramachandran Kannan, S. Vempala, A. Veta · 2002
We propose a new measure for assessing the quality of a clustering. A simple heuristic is shown to give worst-case guarantees under the new measure. Then we present two results regarding the quality of the clustering found by a popular spectral algorithm. One proffers worst case guarantees whilst the other shows that if there exists a "good" clustering then the spectral algorithm will find one close to it.