The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Hi-index | 0.00 |
Among the many issues in high dimensional index structures using Minimum Bounding Rectangle(MBR), the reduction of fan-out and increase of overlapping area are the key factors in reduction of search speed. It is known that the usage of only minimum and maximum distance in MBR’s pruning process lowers the accuracy of search. In this paper, we present an index structure using cell based MBR in which fan-out gets increased and overlapping is avoided, and a search algorithm which reflects the distribution status of data in MBR to the search. The proposed index structure produces MBR as Vector Approximation-file(VA-file)’s cell units and produces child-MBR by dividing cells. The search algorithm raises the search accuracy by executing pruning using centroid of values included in MBR other than the minimum and maximum distance of cell based MBR and query vector in the k-nn query concerned. Through experiment, we find that the proposed search algorithm has improved its search speed and its accuracy in comparison with existing algorithm.