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
Extension to C-means Algorithm for the Use of Similarity Functions
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Probabilistic proximity searching algorithms based on compact partitions
Journal of Discrete Algorithms - SPIRE 2002
Improvements of TLAESA nearest neighbour search algorithm and extension to approximation search
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
Some approaches to improve tree-based nearest neighbour search algorithms
Pattern Recognition
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
On the least cost for proximity searching in metric spaces
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
Parallel k-most similar neighbor classifier for mixed data
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition. In order to decide the class of a new prototype, the k-NN classifier performs an exhaustive comparison between the prototype to classify (query) and the prototypes in the training set T. However, when T is large, the exhaustive comparison is expensive. To avoid this problem, many fast k-NN algorithms have been developed. Some of these algorithms are based on Approximating-Eliminating search. In this case, the Approximating and Eliminating steps rely on the triangle inequality. However, in soft sciences, the prototypes are usually described by qualitative and quantitative features (mixed data), and sometimes the comparison function does not satisfy the triangle inequality. Therefore, in this work, a fast k most similar neighbour classifier for mixed data (AEMD) is presented. This classifier consists of two phases. In the first phase, a binary similarity matrix among the prototypes in T is stored. In the second phase, new Approximating and Eliminating steps, which are not based on the triangle inequality, are presented. The proposed classifier is compared against other fast k-NN algorithms, which are adapted to work with mixed data. Some experiments with real datasets are presented