Description: Model-Based Clustering is a clustering approach that assumes that the data is generated from a mixture of underlying probability distributions. This method is based on the idea that each group or cluster in the data can be represented by a specific statistical distribution, allowing for a more flexible and accurate modeling of the data structure. Unlike simpler clustering methods, such as k-means, which assign data points to clusters based on distances, model-based clustering uses statistical techniques to infer the probability that a data point belongs to a particular cluster. This is often achieved through algorithms like Expectation-Maximization (EM), which optimize the model parameters to maximize the likelihood of the observed data. This approach is particularly useful in situations where the data presents inherent complexity, such as in high-dimensional data or when clusters have non-spherical shapes. Additionally, model-based clustering allows for the incorporation of prior information through prior distributions, making it robust and adaptable to various contexts and types of data.
