Examples of using the Shark-ML library for dealing with the clustering task samples

The Shark-ML library implements two clustering algorithms: hierarchical clustering and the k-means algorithm.

Hierarchical clustering with Shark-ML

The Shark-ML library implements the hierarchical clustering approach in the following way: first, we need to put our data into a space-partitioning tree. For example, we can use the object of the LCTree class, which implements binary space partitioning. Also, there is the KHCTree class, which implements kernel-induced feature space partitioning. The constructor of this class takes the data for partitioning and an object that implements some stopping criteria for the tree construction. We use the...