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
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 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
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Some approaches to best-match file searching
Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Similarity Search without Tears: The OMNI Family of All-purpose Access Methods
Proceedings of the 17th International Conference on Data Engineering
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
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th 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
Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances
The VLDB Journal — The International Journal on Very Large Data Bases
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVA file: an index structure for high-dimensional datasets
Knowledge and Information Systems
Indexing High-Dimensional Data for Efficient In-Memory Similarity Search
IEEE Transactions on Knowledge and Data Engineering
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Extending metric index structures for efficient range query processing
Knowledge and Information Systems
Reference-based indexing of sequence databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
A compact multi-resolution index for variable length queries in time series databases
Knowledge and Information Systems
When is nearest neighbors indexable?
ICDT'05 Proceedings of the 10th international conference on Database Theory
Effective monitoring by efficient fingerprint matching using a forest of NAQ-trees
Journal of Intelligent Information Systems
Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble
Expert Systems with Applications: An International Journal
A partitioning method for high dimensional data
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Integrating wavelets with clustering and indexing for effective content-based image retrieval
Knowledge-Based Systems
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Similarity search (e.g., k-nearest neighbor search) in high-dimensional metric space is the key operation in many applications, such as multimedia databases, image retrieval and object recognition, among others. The high dimensionality and the huge size of the data set require an index structure to facilitate the search. State-of-the-art index structures are built by partitioning the data set based on distances to certain reference point(s). Using the index, search is confined to a small number of partitions. However, these methods either ignore the property of the data distribution (e.g., VP-tree and its variants) or produce non-disjoint partitions (e.g., M-tree and its variants, DBM-tree); these greatly affect the search efficiency. In this paper, we study the effectiveness of a new index structure, called Nested-Approximate-eQuivalence-class tree (NAQ-tree), which overcomes the above disadvantages. NAQ-tree is constructed by recursively dividing the data set into nested approximate equivalence classes. The conducted analysis and the reported comparative test results demonstrate the effectiveness of NAQ-tree in significantly improving the search efficiency.