@article {10.3844/jcssp.2005.89.97,
article_type = {journal},
title = {A Class of Region-preserving Space Transformations for Indexing High-dimensional Data},
author = {Orlandic, Ratko and Lukaszuk, Jack},
volume = {1},
number = {1},
year = {2005},
month = {Mar},
pages = {89-97},
doi = {10.3844/jcssp.2005.89.97},
url = {https://thescipub.com/abstract/jcssp.2005.89.97},
abstract = {This study introduces a class of region preserving space transformation (RPST) schemes for
accessing high-dimensional data. The access methods in this class differ with respect to their space-partitioning
strategies. The study develops two new static partitioning schemes that can split each
dimension of the space within linear space complexity. They also support an effective mechanism for
handling skewed data in heavily sparse spaces. The techniques are experimentally compared to the
Pyramid Technique, which is another example of static partitioning designed for high-dimensional
data. On real high-dimensional data, the proposed RPST schemes outperform the Pyramid Technique
by a significant margin.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}