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Proceedings of

International Conference on Advances in Computing and Communication ICACC 2013

"INTROSPECTION OF VARIOUS K-NEAREST NEIGHBOR TECHNIQUES"

M.VINYA BABU SUHAS G. KULKARNI
DOI
10.15224/978-981-07-6260-5-65
Pages
89 - 92
Authors
2
ISBN
978-981-07-6260-5

Abstract: “K-Nearest Neighbor (kNN) technique is used in different application areas, as it is very simple, highly efficient and effective. Its main advantage is simplicity but the disadvantages can’t be ignored. Hence many researchers proposed different forms of kNN technique under various situations. Broadly kNN techniques are categorized into structure based and non-structure based (structure less) techniques. The objective of this paper is to introspect the key idea, pros and cons and target data or application area behind every kNN technique. Principal Axix Tree (PAT), Orthogonal Structure Tree(OST) , Nearest Feature Line(NFL), Center Line (CL) ,k-d Tree, Ball Tree, Tunable NN etc. are structure based techniques whereas weighted kNN, Model based kNN, Ranked NN (RNN),Condensed NN, Reduced NN, Pseudo/Generalized NN, Clustered kNN (CkNN) , Mutual kNN ( MkNN), constrained RkNN etc. are structure less techniques developed on the basis of kNN. Structure based techniques reduces the computational c”

Keywords: nearest neighbor, kNN, Data Mining

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