Simple, Scalable and Effective Clustering via One-Dimensional
  Projections
Moses Charikar Monika Henzinger Lunjia Hu Maxmilian Vötsch Erik Waingarten
Clustering is a fundamental problem in unsupervised machine learning with
many applications in data analysis. Popular clustering algorithms such as
Lloyd's algorithm and $k$-means++ can take $\Omega(ndk)$ time when clustering
$n$ points in a $d$-dimensional space (represented by an $n\times d$ matrix
$X$) into $k$ clusters. In applications with moderate to large $k$, the
multiplicative $k$ factor can become very expensive. We introduce a simple
randomized clustering algorithm that provably runs in expected time
$O(\mathrm{nnz}(X) + n\log n)$ for arbitrary $k$. Here $\mathrm{nnz}(X)$ is the
total number of non-zero entries in the input dataset $X$, which is upper
bounded by $nd$ and can be significantly smaller for sparse datasets. We prove
that our algorithm achieves approximation ratio $\smash{\widetilde{O}(k^4)}$ on
any input dataset for the $k$-means objective. We also believe that our
theoretical analysis is of independent interest, as we show that the
approximation ratio of a $k$-means algorithm is approximately preserved under a
class of projections and that $k$-means++ seeding can be implemented in
expected $O(n \log n)$ time in one dimension. Finally, we show experimentally
that our clustering algorithm gives a new tradeoff between running time and
cluster quality compared to previous state-of-the-art methods for these tasks.