K- Nearest Neighbor (KNN)

K-nearest neighbor is measured with respect to value of k, that define how many nearest neighbors need to be examine to describe class of a sample data point . Nearest neighbor technique is divided into two categories i.e., structure based KNN and structure less KNN.

The structure based technique deals with the basic structure of the data where the structure has less mechanism which associated with training data samples.

In structure less technique entire data is categorized into sample data point and training data, distance is calculated between sample points and all training points and the point with smallest distance is known as nearest neighbor.

One of the main advantages of KNN technique is that it is effective for large training data and robust to noisy training data.

 Scaling KNN queries over enormous high dimensional multimedia datasets is a stimulating issue for KNN classifiers.