| knn {clustering} | R Documentation |
K-Nearest Neighbor or K-NN is a Supervised Non-linear classification
algorithm. K-NN is a Non-parametric algorithm i.e it doesn’t make any
assumption about underlying data or its distribution. It is one of
the simplest and widely used algorithm which depends on it’s k value
(Neighbors) and finds it’s applications in many industries like
finance industry, healthcare industry etc.
knn(x,
k = 16,
jaccard = 0.6);In the KNN algorithm, K specifies the number of neighbors and its algorithm is as follows:
+ Choose the number K Of neighbor. + Take the K Nearest Neighbor Of unknown data point according To distance. + Among the K-neighbors, Count the number Of data points In Each category. + Assign the New data point To a category, where you counted the most neighbors. For the Nearest Neighbor classifier, the distance between two points Is expressed in the form of Euclidean Distance.