In model-based clustering, we assume that data is generated by a model and tries to get the model from the data. The right model will fit the data better than other models.
In the K-means algorithm, we provide the initial set of clusters and K-means provides us with the data points in the clusters. Think about a case where clusters are not distributed normally, then the improvement of the cluster will not be effective using k-means. In this scenario, the model-based clustering algorithm will do the job. You can think of another method when dividing the clusters, that is, hierarchical clustering in which we will need to find the overlapping information. This situation will also be covered by model-based clustering algorithms.
If all the components are not well separated, a cluster can consist of multiple mixture components. In simple terms, in model-based clustering, data is a mixture of two or more components. Each component has an associated probability and...
