What is EM clustering algorithm?

The EM algorithm extends this basic approach to clustering in two important ways: Instead of assigning examples to clusters to maximize the differences in means for continuous variables, the EM clustering algorithm computes probabilities of cluster memberships based on one or more probability distributions.

What is EM algorithm explain it?

The Expectation-Maximization (EM) algorithm is a way to find maximum-likelihood estimates for model parameters when your data is incomplete, has missing data points, or has unobserved (hidden) latent variables. It is an iterative way to approximate the maximum likelihood function.

What is EM algorithm in data mining?

What is an EM algorithm? The Expectation-Maximization (EM) algorithm is defined as the combination of various unsupervised machine learning algorithms, which is used to determine the local maximum likelihood estimates (MLE) or maximum a posteriori estimates (MAP) for unobservable variables in statistical models.

Is K means an EM algorithm?

EM and K-means are similar in the sense that they allow model refining of an iterative process to find the best congestion. However, the K-means algorithm differs in the method used for calculating the Euclidean distance while calculating the distance between each of two data items; and EM uses statistical methods.

What are the advantages and applications of EM algorithm?

Advantages of EM algorithm – It is always guaranteed that likelihood will increase with each iteration. The E-step and M-step are often pretty easy for many problems in terms of implementation. Solutions to the M-steps often exist in the closed form.

What are the two steps in EM algorithm?

The EM algorithm is an iterative approach that cycles between two modes. The first mode attempts to estimate the missing or latent variables, called the estimation-step or E-step. The second mode attempts to optimize the parameters of the model to best explain the data, called the maximization-step or M-step.

Why do we need generalized EM algorithm?

The EM algorithm is used for obtaining maximum likelihood estimates of parameters when some of the data is missing. More generally, however, the EM algorithm can also be applied when there is latent, i.e. unobserved, data which was never intended to be observed in the first place.

What is the difference between K-means and Expectation Maximization?

Process of K-Means is something like assigning each observation to a cluster and process of EM(Expectation Maximization) is finding likelihood of an observation belonging to a cluster(probability).

What is Gaussian mixture model clustering?

Gaussian mixture models (GMMs) are often used for data clustering. You can use GMMs to perform either hard clustering or soft clustering on query data. To perform hard clustering, the GMM assigns query data points to the multivariate normal components that maximize the component posterior probability, given the data.

What are the advantages of EM algorithm?

Does EM algorithm always converge?

So yes, EM algorithm always converges, even though it might converge to bad local extrema, which is a different issue.

What are the disadvantages of the EM algorithm?

Disadvantages of EM algorithm –

  • It has slow convergence.
  • It makes convergence to the local optima only.
  • It requires both the probabilities, forward and backward (numerical optimization requires only forward probability).