The k-nearest neighbor algorithm is a classification algorithm that assigns to a given data point the majority class among the k-nearest neighbors. The distance between two points is measured by a metric. Examples of distances include: Euclidean distance, Manhattan distance, Minkowski distance, Hamming distance, Mahalanobis distance, Tanimoto distance, Jaccard distance, tangential distance, and cosine distance. Experiments with various parameters and cross-validation can help to establish which parameter k and which metric should be used.

The dimensionality and position of a data point in the space are determined by its qualities. A large number of dimensions can result in low accuracy of the k-NN algorithm. Reducing the dimensions of qualities of smaller importance can increase accuracy. Similarly, to increase accuracy further, distances for each dimension should be scaled according to the importance of the quality of that dimension.