Model-based clustering involves several steps: 1. Data Collection: Gathering high-dimensional data such as gene expression profiles, genetic mutations, or clinical characteristics. 2. Model Selection: Choosing an appropriate probabilistic model, such as Gaussian Mixture Models (GMMs), to represent the data. 3. Parameter Estimation: Using algorithms like Expectation-Maximization (EM) to estimate the parameters of the chosen model. 4. Cluster Assignment: Assigning each data point to a cluster based on the estimated model parameters.
