  The $k$-means is a popular clustering objective, although it is inherently
non-robust and sensitive to outliers. Its popular seeding or initialization
called $k$-means++ uses $D^{2}$ sampling and comes with a provable $O(\log k)$
approximation guarantee \cite{AV2007}. However, in the presence of adversarial
noise or outliers, $D^{2}$ sampling is more likely to pick centers from distant
outliers instead of inlier clusters, and therefore its approximation guarantees
\textit{w.r.t.} $k$-means solution on inliers, does not hold.
  Assuming that the outliers constitute a constant fraction of the given data,
we propose a simple variant in the $D^2$ sampling distribution, which makes it
robust to the outliers. Our algorithm runs in $O(ndk)$ time, outputs $O(k)$
clusters, discards marginally more points than the optimal number of outliers,
and comes with a provable $O(1)$ approximation guarantee.
  Our algorithm can also be modified to output exactly $k$ clusters instead of
$O(k)$ clusters, while keeping its running time linear in $n$ and $d$. This is
an improvement over previous results for robust $k$-means based on LP
relaxation and rounding \cite{Charikar}, \cite{KrishnaswamyLS18} and
\textit{robust $k$-means++} \cite{DeshpandeKP20}. Our empirical results show
the advantage of our algorithm over $k$-means++~\cite{AV2007}, uniform random
seeding, greedy sampling for $k$ means~\cite{tkmeanspp}, and robust
$k$-means++~\cite{DeshpandeKP20}, on standard real-world and synthetic data
sets used in previous work. Our proposal is easily amenable to scalable,
faster, parallel implementations of $k$-means++ \cite{Bahmani,BachemL017} and
is of independent interest for coreset constructions in the presence of
outliers \cite{feldman2007ptas,langberg2010universal,feldman2011unified}.
