In KNN we calculate the distance between points to find the nearest neighbor, and in K-Means we find the distance between points to group data points into clusters based on similarity. It is vital to choose the right distance measure as it impacts the results of our algorithm. 1-Calculate the distance between test data and each row of training data. Here we will use Euclidean distance as our distance metric since it's the most popular method. The other metrics that can be used are Chebyshev, cosine, etc. 2-Sort the calculated distances in ascending order based on distance values. 3-Get top k rows from the sorted array.

Dec 01, 2019 · Details. This function takes a number of arguments and it returns the indices and distances of the k-nearest-neighbors for each observation. If TEST_data is NULL then the indices-distances for the train data will be returned, whereas if TEST_data is not NULL then the indices-distances for the TEST_data will be returned. Mar 26, 2018 · Calculate the distance between test data and each row of training data. Here we will use Euclidean distance as our distance metric since it’s the most popular method. The other metrics that can be used are Chebyshev, cosine, etc. Sort the calculated distances in ascending order based on distance values; Get top k rows from the sorted array