A New Approach to Indexing in High-Dimensional Space

. SVI is a promising new scheme for indexing high-dimensional points and vectors for use in vector retrieval and for finding the k-nearest neighbours. SVI performs an approximate search; that is, it trades off the completeness of the search for speed. The indexing scheme is built around a rule that was found by applying data mining techniques to sets of random vectors. This approach could well lead to further improvements in the indexing scheme. 1 Introduction This paper introduces a promising new scheme for indexing high-dimensional points and vectors called sub-vector indexing (SVI). SVI is used to reduce the time expended in processes such as vector retrieval and finding the k-nearest neighbours. Also described is the approach taken in developing SVI, for it is believed that this approach may lead to further improvements in the indexing scheme. It must be noted that the search performed in SVI is incomplete (or approximate), for it generally fails to locate some of the desired ite..