Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Disorder inequality: a combinatorial approach to nearest neighbor search
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A discriminative framework for clustering via similarity functions
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Combinatorial algorithms for nearest neighbors, near-duplicates and small-world design
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Distributed similarity search in high dimensions using locality sensitive hashing
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Brute force and indexed approaches to pairwise document similarity comparisons with MapReduce
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Combinatorial Framework for Similarity Search
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Similarity search on Bregman divergence: towards non-metric indexing
Proceedings of the VLDB Endowment
Web-scale distributional similarity and entity set expansion
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
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Nearest Neighbor Search is a task to design a pair of algorithms for the following scenario. A dataset S of n objects is given and preprocessing algorithm is applied. Then a query object q is presented and search algorithm is used to find the nearest to q object in the dataset S. Any solution to Nearest Neighbor Search Problem consists of two parts: a framework and a pair of algorithms. A framework provides a specific formalization of the problem such as object representation, distance (or similarity) function, dataset properties and restrictions, computation cost model, dynamic aspects and solution requirements. There are thousands of possible framework variations. Any practical application can lead to its unique problem formalization. Thus, it is important to have a universal algorithmic toolbox that can be adapted across all existing and future frameworks. In this letter we state four fundamental algorithmic ideas that underly a vast majority of published solutions to Nearest Neighbor Search.