The Reduced Nearest Neighbor (RNN) is improvementover CNN; it includes one more step that is elimination of thepatterns which are not affecting the training data set result. Theanother technique called Model Based kNN selects similaritymeasures and create a 'similarity matrix' from given trainingset. Then, in the same category, largest local neighbor is foundthat covers large number of neighbors and a data tuple islocated with largest global neighborhood. These steps arerepeated until all data tuples are grouped. Once data is formedusing model, kNN is executed to specify category of unknownsample. Subash C. Bagui and Sikha Bagui [8] improve thekNN by introducing the concept of ranks. The method pools allthe observations belonging to different categories and assignsrank to each category data in ascending order. Thenobservations are counted and on the basis of ranks class isassigned to unknown sample. It is very much useful in case ofmulti-variants data. Yong zeng, Yupu Zeng and Liang Zhoudefine the new concept to classify sample data point. Themethod introduces the pseudo neighbor, which is not the actualnearest neighbor; but a new nearest neighbor is selected on thebasis of value of weighted sum of distances of kNN ofunclassified patterns in each class. Then Euclidean distance isevaluated and pseudo neighbor with greater weight is foundand classified for unknown sample. In the technique purposedby Zhou Yong [11], Clustering is used to calculate nearestneighbor. The steps include, first of all removing the sampleswhich are lying near to the border, from training set. Clustereach training set by k value clustering and all cluster centersform new training set. Assign weight to each cluster accordingto number of training samples each cluster have..


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