This paper investigates the following natural greedy procedure for clustering in the bi-criterion setting: iteratively grow a set of centers, in each round adding the center from a candidate set that maximally decreases clustering cost. In the case of $k$-medians and $k$-means, the key results are as follows. #R##N#$\bullet$ When the method considers all data points as candidate centers, then selecting $\mathcal{O}(k\log(1/\varepsilon))$ centers achieves cost at most $2+\varepsilon$ times the optimal cost with $k$ centers. #R##N#$\bullet$ Alternatively, the same guarantees hold if each round samples $\mathcal{O}(k/\varepsilon^5)$ candidate centers proportionally to their cluster cost (as with $\texttt{kmeans++}$, but holding centers fixed). #R##N#$\bullet$ In the case of $k$-means, considering an augmented set of $n^{\lceil1/\varepsilon\rceil}$ candidate centers gives $1+\varepsilon$ approximation with $\mathcal{O}(k\log(1/\varepsilon))$ centers, the entire algorithm taking $\mathcal{O}(dk\log(1/\varepsilon)n^{1+\lceil1/\varepsilon\rceil})$ time, where $n$ is the number of data points in $\mathbb{R}^d$. #R##N#$\bullet$ In the case of Euclidean $k$-medians, generating a candidate set via $n^{\mathcal{O}(1/\varepsilon^2)}$ executions of stochastic gradient descent with adaptively determined constraint sets will once again give approximation $1+\varepsilon$ with $\mathcal{O}(k\log(1/\varepsilon))$ centers in $dk\log(1/\varepsilon)n^{\mathcal{O}(1/\varepsilon^2)}$ time. #R##N#Ancillary results include: guarantees for cluster costs based on powers of metrics; a brief, favorable empirical evaluation against $\texttt{kmeans++}$; data-dependent bounds allowing $1+\varepsilon$ in the first two bullets above, for example with $k$-medians over finite metric spaces.
['Daniel J. Hsu', 'Matus Telgarsky']
Greedy bi-criteria approximations for $k$-medians and $k$-means