These indexing algorithms create divisions within the data that serve to decrease the time necessary for nearest neighbor calculations. By assigning each training instance to a section of the global space, the indexing algorithms decrease the number of training instances the testing instances need to be compared against. Because these algorithms do not necessarily create clusters, they cannot be classified as standard clustering algorithms. These indexing algorithms are instead used to augment standard clustering or relevancy filtering algorithms, decreasing the time necessary to run them.
