Resilient k-d trees: k-means in space revisited
We propose a k-d tree variant that is resilient to a pre-described number of memory corruptions while still using only linear space. While the data structure is of independent interest, we demonstrate its use in the context of high-radiation environments. Our experimental evaluation demonstrates that the resulting approach leads to a significantly higher resiliency rate compared to previous results. This is especially the case for large-scale multi-spectral satellite data, which renders the proposed approach well-suited to operate aboard today's satellites.
data mining clustering resilient algorithms and data structures