```
tags: [AI/Tasks/Clustering, AI/Algorithms/GMM, AI/ML/UnSupervisedLearning, ]
aliases: [GMM]
```

Gaussian mixture model (GMM) is a Clustering algorithm using probability density estimation for datasets where data is composed of a mixture of several Gaussian distributions and is similar to K-Means algorithm with the main difference being that it accounts for variance.

Advantages:

- It accounts for variance, unlike K-Means.
- It can provide probability for each data point’s membership in the clusters.
- It can identify overlapping clusters.

Disadvantages:

- It requires the number of clusters or mixture components to be defined.
- The covariance type must be defined.
- It’s only useful when distribution type is known and compatible(Mixture of Gaussian distributions with different means and variances).
- It’s computationally expensive.
- Requires large amount of data for estimating number of clusters.

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