Faster Algorithms for the Constrained k-means Problem

Abstract: The classical center based clustering problems such as $k$-means/median/center assume that the optimal clusters satisfy the locality property that the points in the same cluster are close to each other. A number of clustering problems arise in machine learning where the optimal clusters do not follow such a locality property. Consider a variant of the $k$-means problem that may be regarded as a general version of such problems. Here, the optimal clusters $O_1, ..., O_k$ are an arbitrary partition of the dataset and the goal is to output $k$-centers $c_1, ..., c_k$ such that the objective function $$\sum_{i=1}^{k} \sum_{x \in O_{i}} ||x - c_{i}||<sup>2$$ is minimized. It is not difficult to argue that any algorithm (without knowing the optimal clusters) that outputs a single set of $k$ centers, will not behave well as far as optimizing the above objective function is concerned. However, this does not rule out the existence of algorithms that output a list of such $k$ centers such that at least one of these $k$ centers behaves well. Given an error parameter $\varepsilon &gt; 0$, let $\ell$ denote the size of the smallest list of $k$-centers such that at least one of the $k$-centers gives a $(1+\varepsilon)$ approximation w.r.t. the objective function above. In this paper, we show an upper bound on $\ell$ by giving a randomized algorithm that outputs a list of $2<sup>{\tilde{O}(k/\varepsilon)}$ $k$-centers. We also give a closely matching lower bound of $2<sup>{\tilde{\Omega}(k/\sqrt{\varepsilon})}$. Moreover, our algorithm runs in time $$O \left(n d \cdot 2^{{\tilde{O}(k/\varepsilon)}} \right)$$. This is a significant improvement over the previous result of Ding and Xu who gave an algorithm with running time $$O \left(n d \cdot (\log{n})^{k} \cdot 2^{{poly(k/\varepsilon)}} \right)$$ and output a list of size $$O \left((\log{n})^k \cdot 2^{{poly(k/\varepsilon)}} \right)$$.