@online{KolevArXiv2015,
TITLE = {A Note On Spectral Clustering},
AUTHOR = {Kolev, Pavel and Mehlhorn, Kurt},
LANGUAGE = {eng},
URL = {http://arxiv.org/abs/1509.09188},
EPRINT = {1509.09188},
EPRINTTYPE = {arXiv},
YEAR = {2015},
ABSTRACT = {Let $G=(V,E)$ be an undirected graph, $\lambda_k$ the $k$th smallest eigenvalue of the normalized Laplacian matrix of $G$, and $\rho(k)$ the smallest value of the maximal conductance over all $k$-way partitions $S_1,\dots,S_k$ of $V$. Peng et al. [PSZ15] gave the first rigorous analysis of $k$-clustering algorithms that use spectral embedding and $k$-means clustering algorithms to partition the vertices of a graph $G$ into $k$ disjoint subsets. Their analysis builds upon a gap parameter $\Upsilon=\rho(k)/\lambda_{k+1}$ that was introduced by Oveis Gharan and Trevisan [GT14]. In their analysis Peng et al. [PSZ15] assume a gap assumption $\Upsilon\geq\Omega(\mathrm{APR}\cdot k^3)$, where $\mathrm{APR}>1$ is the approximation ratio of a $k$-means clustering algorithm. We exhibit an error in one of their Lemmas and provide a correction. With the correction the proof by Peng et al. [PSZ15] requires a stronger gap assumption $\Upsilon\geq\Omega(\mathrm{APR}\cdot k^4)$. Our main contribution is to improve the analysis in [PSZ15] by an $O(k)$ factor. We demonstrate that a gap assumption $\Psi\geq \Omega(\mathrm{APR}\cdot k^3)$ suffices, where $\Psi=\rho_{avr}(k)/\lambda_{k+1}$ and $\rho_{avr}(k)$ is the value of the average conductance of a partition $S_1,\dots,S_k$ of $V$ that yields $\rho(k)$.},
}