Clustering with k-means 

k-Nearest Neighbors (kNN) is a well-known clustering method. It is based on finding similarities in data points, or what we call the feature similarity. Thus, this algorithm is simple, and is widely used to solve many classification problems, like recommendation systems, anomaly detection, credit ratings, and so on . However, it requires a high amount of memory. While it is a supervised learning model, it should be fed by labeled data, and the outputs are known. We only need to map the function that relates the two parties. A kNN algorithm is non-parametric. Data is represented as feature vectors. You can see it as a mathematical representation:

The classification is done like a vote; to know the class of the data selected, you must first compute the distance between the selected item and the other, training item. But how can we calculate these distances?

Generally, we have two major methods for calculating. We can use the Euclidean distance:

Or, we can use the cosine similarity:

The second step is choosing k the nearest distances (k can be picked arbitrarily). Finally, we conduct a vote, based on a confidence level. In other words, the data will be assigned to the class with the largest probability.