The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd 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
Contrast Plots and P-Sphere Trees: Space vs. Time in Nearest Neighbour Searches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
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
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
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In this paper, we propose Diagonal Ordering, a new technique for K-Nearest-Neighbor (KNN) search in a high-dimensional space. Our solution is based on data clustering and a particular sort order of the data points, which is obtained by "slicing" each cluster along the diagonal direction. In this way, we are able to transform the high-dimensional data points into one-dimensional space and index them using a B+-tree structure. KNN search is then performed as a sequence of one-dimensional range searches. Advantages of our approach include: (1) irrelevant data points are eliminated quickly without extensive distance computations; (2) the index structure can effectively adapt to different data distributions; (3) on-line query answering is supported, which is a natural byproduct of the iterative searching algorithm. We conduct extensive experiments to evaluate the Diagonal Ordering technique and demonstrate its effectiveness.