Name
Abstract | ||
|---|---|---|
Given a query point q, finding the nearest neighbor (NN) object is one of the most important problem in computer science. In this paper, a bottom-up search algorithm for processing NN query in R-trees is presented. An additional data structure, hash, is introduced to increase the pruning capability of the proposed algorithm. Based on hash, whole data space is disjointly partitioned into n × n cells. Each cell contains the pointers of leaf nodes which intersect with the cell. The experiment shows that the proposed approach outperforms the existing NN search algorithms including the BFS algorithm which is known as I/O optimal algorithm. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11733836_68 | DASFAA |
Keywords | Field | DocType |
nn query,query point q,bottom-up search algorithm,existing nn search,bfs algorithm,n cell,o optimal algorithm,additional data structure,proposed algorithm,nearest neighbor query,nearest neighbor,bottom up,data structure,search algorithm | k-nearest neighbors algorithm,R-tree,Data mining,Fixed-radius near neighbors,Search algorithm,Best bin first,Computer science,Breadth-first search,Hash function,Database,Nearest neighbor search | Conference |
Volume | ISSN | ISBN |
3882 | 0302-9743 | 3-540-33337-1 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| MoonBae Song | 1 | 123 | 18.05 |
| KwangJin Park | 2 | 174 | 23.16 |
| SeokJin Im | 3 | 39 | 6.42 |
| Ki-Sik Kong | 4 | 219 | 20.04 |