Hands-On Machine Learning with C++

Model-based clustering algorithms

Model-based algorithms assume that there is a particular mathematical model of the cluster in the data space and try to maximize the likelihood of this model and the available data. Often, this uses the apparatus of mathematical statistics.

The EM (Expectation–Maximization) algorithm assumes that the dataset can be modeled using a linear combination of multidimensional normal distributions. Its purpose is to estimate distribution parameters that maximize the likelihood function used as a measure of model quality. In other words, it assumes that the data in each cluster obeys a particular distribution law—namely, the normal distribution. With this assumption, it is possible to determine the optimal parameters ...