Archives for Gaussian mixture modelling clustering


The expectation-maximization (EM) algorithm is an elegant algorithm that maximizes the likelihood function for problems with latent or hidden variables.


In the field of unsupervised learning, probabilistic models which represent the probability of the presence of clusters within the overall population can be considered as mixture model.


In recent times, there has been a lot of emphasis on Unsupervised learning. Studies like customer segmentation, pattern recognition has been a widespread example of this which in simple terms we can refer to as Clustering. We used to solve our problem using a basic algorithm like K-means or Hierarchical Clustering. With the introduction of Gaussian mixture modelling clustering data points have become simpler as they can handle even oblong clusters. It works in the same principle as K-means but has some of the advantages over it.
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