An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
A cost model for query processing in high dimensional data spaces
ACM Transactions on Database Systems (TODS)
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Introduction to Algorithms
Fixed Queries Array: A Fast and Economical Data Structure for Proximity Searching
Multimedia Tools and Applications
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Proximity Matching Using Fixed-Queries Trees
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Spaghettis: An Array Based Algorithm for Similarity Queries in Metric Spaces
SPIRE '99 Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
A Dynamic Pivot Selection Technique for Similarity Search
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
Reference-based indexing for metric spaces with costly distance measures
The VLDB Journal — The International Journal on Very Large Data Bases
Analyzing Metric Space Indexes: What For?
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Hi-index | 0.00 |
This paper presents an asymptotic analysis for the nearest neighbor search with pivot-based indexes. We extend a previous analysis based on range queries with fixed tolerance radius, because there is a probability that the nearest neighbor is missed. We introduce a probabilistic analysis and then we show the expected search cost for range-optimal algorithms. Finally, we also show the analysis of the proposed search algorithm taking into account the extra CPU time, which leads to further insights on the efficiency of different implementations of this algorithm.