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
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
On the effects of dimensionality reduction on high dimensional similarity search
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
Fast Nearest Neighbor Search in High-Dimensional Space
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th 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
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient High-Dimensional Indexing by Superimposing Space-Partitioning Schemes
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Clustering high-dimensional data using an efficient and effective data space reduction
Proceedings of the 14th ACM international conference on Information and knowledge management
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Nearest neighbor search has a wide variety of applications. Unfortunately, the majority of search methods do not scale well with dimensionality. Recent efforts have been focused on finding better approximate solutions that improve the locality of data using dimensionality reduction. However, it is possible to preserve the locality of data and find exact nearest neighbors in high dimensions without dimensionality reduction. This paper introduces a novel high-performance technique to find exact k-nearest neighbors in both low and high dimensional spaces. It relies on a new method for data-sensitive space partitioning based on explicit data clustering, which is introduced in the paper for the first time. This organization supports effective reduction of the search space before accessing secondary storage. Costly Euclidean distance calculations are reduced through efficient processing of a lightweight memory-based filter. The algorithm outperforms sequential scan and the VA-File in high-dimensional situations. Moreover, the results with dynamic loading of data show that the technique works well on dynamic datasets as well.