An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
A fast branch & bound nearest neighbour classifier in metric spaces
Pattern Recognition Letters
Comparison of fast nearest neighbour classifiers for handwritten character recognition
Pattern Recognition Letters
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
A modification of the LAESA algorithm for approximated k-NN classification
Pattern Recognition Letters
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
Extending LAESA Fast Nearest Neighbour Algorithm to Find the k Nearest Neighbours
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Comparison of AESA and LAESA search algorithms using string and tree-edit-distances
Pattern Recognition Letters
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Fast k most similar neighbor classifier for mixed data (tree k-MSN)
Pattern Recognition
Fast k most similar neighbor classifier for mixed data based on approximating and eliminating
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Nearest neighbour (NN) searches and k nearest neighbour (k-NN) searches are widely used in pattern recognition and image retrieval. An NN (k-NN) search finds the closest object (closest k objects) to a query object. Although the definition of the distance between objects depends on applications, its computation is generally complicated and time-consuming. It is therefore important to reduce the number of distance computations. TLAESA (Tree Linear Approximating and Eliminating Search Algorithm) is one of the fastest algorithms for NN searches. This method reduces distance computations by using a branch and bound algorithm. In this paper we improve both the data structure and the search algorithm of TLAESA. The proposed method greatly reduces the number of distance computations. Moreover, we extend the improved method to an approximation search algorithm which ensures the quality of solutions. Experimental results show that the proposed method is efficient and finds an approximate solution with a very low error rate.