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What are the two main steps of the Expectation Maximization algorithm?
Which industry can benefit from using Expectation Maximization to identify different types of financial behavior?
What is the purpose of ‘Cluster Purity’ in evaluating the performance of the Expectation Maximization algorithm?
Why is selecting the number of clusters a challenging task in Expectation Maximization?
How does Expectation Maximization handle missing or hidden data?
What is the role of ‘Log-Likelihood’ in measuring the performance of the Expectation Maximization algorithm?
In which real-world industry can Expectation Maximization be used to predict disease outbreaks?
What is a major limitation of Expectation Maximization when dealing with high-dimensional data?
What type of model is commonly used in the Expectation Maximization algorithm?
In the context of EM, what does the term ‘latent variables’ refer to?
How does the ‘Maximization’ step in EM work?
What is the main advantage of using the EM algorithm over K-means clustering?
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