K nearest neighbor algorithm is a classification algorithm that works in a way that a new data point is assigned to a neighboring group to which it is most similar.
In K nearest neighbors, K can be an integer greater than 1. So, for every new data point, we want to classify, we compute to which neighboring group it is closest.
Let us classify an object using the following example. Consider there are three clusters:
1) Football
2) Basketball
3) Tennis ball
4) Kluster 1
Let the new data point to be classified is a black ball. We use KNN to classify it. Assume K = 5 (initially).
Next, we find the K (five) nearest data points, as shown.
Kluster 2
Observe that all five selected points do not belong to the same cluster. There are three tennis balls and one each of basketball and football.
When multiple classes are involved, we prefer the majority. Here the majority is with the tennis ball, so the new data point is assigned to this cluster.