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
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Using the fractal dimension to cluster datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A cost model for query processing in high dimensional data spaces
ACM Transactions on Database Systems (TODS)
ACM Computing Surveys (CSUR)
Fractal prefetching B+-Trees: optimizing both cache and disk performance
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Clustering for Approximate Similarity Search in High-Dimensional Spaces
IEEE Transactions on Knowledge and Data Engineering
Approximate Nearest Neighbor Searching in Multimedia Databases
Proceedings of the 17th International Conference on Data Engineering
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
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
VLDB '93 Proceedings of the 19th 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
Constrained Nearest Neighbor Queries
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Indexing high-dimensional data for main-memory similarity search
Information Systems
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In the recent years, several techniques have been developed for efficient similarity search in high-dimensional data spaces. Some of the techniques, based on the idea of vector approximation via quantization, have been shown to be the most effective. The VA-file was the first technique to use vector approximation. The IQ-tree and the A-tree are subsequent techniques that impose a directory structure over the quantized VA-file representation. The performance gains of the IQ-tree result mainly from an optimized I/O strategy permitted by the directory structure. Those of the A-tree result mainly from exploiting the clustering of the data itself. In our work, first we evaluate the relative performance of these two enhanced approaches over high-dimensional data sets with different clustering characteristics. Second, we present the Clustered IQ-Tree, which is an indexing strategy that combines the best features of the IQ-tree and the A-tree, leading to better query performance than the former and more stable performance than the latter across different types of data sets.