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Survey of Nearest Neighbor Techniques
The nearest neighbor (NN) technique is very simple, highly efficient and
effective in the field of pattern recognition, text categorization, object
recognition etc. Its simplicity is its main advantage, but the disadvantages
can't be ignored even. The memory requirement and computation complexity also
matter. Many techniques are developed to overcome these limitations. NN
techniques are broadly classified into structure less and structure based
techniques. In this paper, we present the survey of such techniques. Weighted
kNN, Model based kNN, Condensed NN, Reduced NN, Generalized NN are structure
less techniques whereas k-d tree, ball tree, Principal Axis Tree, Nearest
Feature Line, Tunable NN, Orthogonal Search Tree are structure based algorithms
developed on the basis of kNN. The structure less method overcome memory
limitation and structure based techniques reduce the computational complexity.Comment: 4 pages, 1 tabl