(b) What is model based clustering? Explain the model based clustering techniques.
Explanation
Model-based clustering is a type of clustering algorithm that uses a probabilistic model to identify clusters in the data. It is an unsupervised learning algorithm that involves creating a model that describes the underlying distribution of the data and then using this model to identify clusters. The model-based clustering techniques are widely used in various applications such as image segmentation, gene expression analysis, and customer segmentation.
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