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A Study on K-Nearest Neighbor Techniques with Other Techniques

Kousalyadevi .B, Dr. A. Finny Belwin, Dr.A. Linda Sherin, Dr. Antony Selvadoss Thanamani

Abstract


Abstract

The closest neighbor (NN) system is basic, exceptionally proficient and feasible in the field of example acknowledgment, content arrangement, object acknowledgment and so forth. Its straightforwardness is its primary preferred position; however, the impediments cannot be overlooked even. The memory necessity and calculation unpredictability are moreover matters. Numerous methods are created to defeat these restrictions. NN procedures are comprehensively ordered into structure less and structure-based procedures. In this paper, we present the review of such procedures. Weighted kNN, Model-based kNN, Dense NN, Reduced NN, Generalized NN are structure less procedures while k-d tree, ball tree, Principal Axis Tree, Closest Feature Line, tunable NN, Orthogonal Search Tree are structure put together calculations created with respect to the premise of kNN. The structure less technique conquer memory confinement and structure-based procedures lessen the computational multifaceted nature.


Keywords


Nearest neighbor (NN), kNN, Model-based kNN, weighted kNN, Condensed NN, Reduced NN.

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