How does the BayesiaLab K-Means discretization algorithm work?
This discretization algorithm is an unsupervised univariate discretization algorithm that consists in applying the classical K-means clustering to one-dimensional continuous data.
The Expectation-Maximization algorithm works as follows:
- Initialization: random creation of K centers
- Expectation: each point is associated with the closest center
- Maximization: each center position is computed as the barycenter of its associated points
Steps 2 and 3 are repeated until convergence is reached.
The discretization thresholds used by BayesiaLab are defined as: