$k$-means clustering is NP-hard in the worst case but previous work has shown efficient algorithms assuming the optimal $k$-means clusters are \emph{stable} under additive or multiplicative perturbation of data. This has two caveats. First, we do not know how to efficiently verify this property of optimal solutions that are NP-hard to compute in the first place. Second, the stability assumptions required for polynomial time $k$-means algorithms are often unreasonable when compared to the ground-truth clusters in real-world data. A consequence of multiplicative perturbation resilience is \emph{center proximity}, that is, every point is closer to the center of its own cluster than the center of any other cluster, by some multiplicative factor $α> 1$. We study the problem of minimizing the Euclidean $k$-means objective only over clusterings that satisfy $α$-center proximity. We give a simple algorithm to find the optimal $α$-center-proximal $k$-means clustering in running time exponential in $k$ and $1/(α- 1)$ but linear in the number of points and the dimension. We define an analogous $α$-center proximity condition for outliers, and give similar algorithmic guarantees for $k$-means with outliers and $α$-center proximity. On the hardness side we show that for any $α' > 1$, there exists an $α\leq α'$, $(α>1)$, and an $\varepsilon_0 > 0$ such that minimizing the $k$-means objective over clusterings that satisfy $α$-center proximity is NP-hard to approximate within a multiplicative $(1+\varepsilon_0)$ factor.