Julia for Data Science

K-means clustering

K-means is the most popular of the clustering techniques because of its ease of use and implementation. It also has a partner by the name of K-medoid. These partitioning methods create level-one partitioning of the dataset. Let's discuss K-means in detail.

K-means algorithm

K-means start with a prototype. It takes centroids of data points from the dataset. This technique is used for the objects lying in the n-dimensional space.

The technique involves choosing the K number of centroids. This K is specified by the user and is chosen considering various factors. It defines how many clusters we want. So, choosing a higher or lower than the required K can lead to undesired results.

Now going forward, each point is assigned to its nearest ...