An Efficient Classifying On Several Datasets in Big Data Assistive K-Nearest Neighbor Algorithm

K nearest neighbors (KNN) is very great learning algorithm. Nowadays it`s been upgraded for several real applications. The large scaling of the datasets from the past k-nearest neighbor strategies is very suitable and natural.as we suggest the whole datasets are portioning into many part after selecting the suitable k-mean cluster for the partitioning of that datasets, then conducted a k-nearest neighbor classification for each parts. So, the medical imaging data andthe big data can be conducted by set of experiments. Therefore, when we talk about the efficiency and accuracy the proposed K-NN classification is working well according to the experimental result of that algorithm.