K-NEAREST NEIGHBOUR SEARCH USING RANDOM PROJECTION FORESTS

The K-NN is frequently implemented in machine learning and pattern recognition applications. Non-parametric methods are employed to evaluate data, classify objects, identify outliers, and offer recommendations. Using a query point and a distance metric (such as Manhattan or Euclidean distance), the K-NN algorithm can identify the 'k' closest points in your dataset. This straightforward concept is both effective and straightforward. K-NN is inefficient on large or high-dimensional datasets, despite its usefulness, as it is required to compare each query point with every dataset point.