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
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Proximity Matching Using Fixed-Queries Trees
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Hybrid Index for Metric Space Databases
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
A Search Engine Index for Multimedia Content
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Clustered pivot tables for I/O-optimized similarity search
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Semi-structured semantic overlay for information retrieval in self-organizing networks
Proceedings of the 21st international conference companion on World Wide Web
Dynamic optimization of queries in pivot-based indexing
Multimedia Tools and Applications
Flexible and efficient string similarity search with alignment-space transform
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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Similarity search is a necessary operation for applications dealing with unstructured data sources. In this paper we present a pivot-based method useful, not only to obtain a good pivot selection without specifying in advance the number of pivots, but also to obtain an insight in the complexity of the metric space. Sparse Spatial Selection (SSS) adapts itself to the dimensionality of the metric space, is dynamic, and it is suitable for secondary memory storage. In this paper we provide experimental results that confirm the advantages of the method with several metric spaces. Moreover, we explain how SSS can be easily parallelized. Finally, in this paper we conceptualize Nested Metric Spaces, and we prove that, in some applications areas, objects can be grouped in different clusters with different associated metric spaces, all of them nested into the general metric space that explains the distances among clusters.