Resilient K-d Trees: K-Means in Space Revisited

We develop a k-d tree variant that is resilient to a pre-described number of memory corruptions while still using only linear space. We show how to use this data structure in the context of clustering in high-radiation environments and demonstrate that our approach leads to a significantly higher resiliency rate compared to previous results.