Enhancement of K nearest neighbour approach to solve the issue of pattern classification

k Nearest Neighbour (kNN) method is a frequently implemented method for improve the pattern classification and solve the problem of imbalanced dataset that has discovered so many classification and clustering applications. In this paper, issues of classification were focused and changes to the nearest neighbouring method were suggested which exploit data from a dataset structure. The issue of class imbalance has recently attracted considerable attention from researchers in data mining strategies. The results of the present experiments using unique client identifier (UCI) repository datasets show that the classifiers generated the work much effective when compared with classic kNN & are much accurate, but not dramatically slow. This produced some fascinating and encouraging outcomes, which inspired more work to develop the kNN process. The purpose of this paper is to suggest versions of kNN which are appropriate for categorization of issues, since it leverage intrinsic of knowledge within data sets, like the composition of the datasets.