What does "Model-Based Clustering" mean?
Table of Contents
Model-based clustering is a method used to group data based on statistical models. Instead of just looking at the raw data, this approach assumes that the data comes from a mixture of different groups, each with its own characteristics.
How It Works
In model-based clustering, each group is represented by a model that describes the data's behavior. These models can help identify patterns and similarities within the groups. The method evaluates which model fits the data best to organize it into meaningful clusters.
Benefits
One of the main advantages of model-based clustering is its flexibility. It can adapt to different types of data and is effective in revealing complex structures. This makes it useful in various fields, such as healthcare, marketing, and social sciences, where understanding groups and patterns is essential.
Applications
Model-based clustering is commonly used to analyze patient data, customer behavior, or social network interactions. By grouping similar observations, researchers and businesses can tailor their strategies and decisions based on insights gained from the clustered data.