Is GMM hard or soft clustering?
At its simplest, GMM is also a type of clustering algorithm. As its name implies, each cluster is modelled according to a different Gaussian distribution. This flexible and probabilistic approach to modelling the data means that rather than having hard assignments into clusters like k-means, we have soft assignments.
Is GMM Parametric?
Definition. A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities.
Why is GMM useful for Gaussian mixture modelling?
The main takeaway in this figure is that the distributions are clearly no longer spherical. GMM has allowed us the relax our restrictions on the covariance matrix allowing the distribution to have a much better fit to the data. This is particularly useful given that the shape of our data was clearly not spherical.
Why is GMM a good alternative to k-means?
I then want to dive into coding the algorithm in Python and we can see how the results differ from k-means and why using GMM may be a good alternative. At its simplest, GMM is also a type of clustering algorithm. As its name implies, each cluster is modelled according to a different Gaussian distribution.
How to make sense of text data using GMM?
Gaussian Mixture Modelling (GMM). Making Sense of Text Data using… | by Daniel Foley | Towards Data Science In a previous post, I discussed k-means clustering as a way of summarising text data. I also talked a bout some of the limitations of k-means and in what situations it may not be the most appropriate solution.
What’s the difference between GMM and spherical clusters?
As you can see, compared to Figure 2 modelled using spherical clusters, GMM is much more flexible allowing us to generate much better fitting distributions. Ok, now we are going to get straight into coding our GMM class in Python. As always, we start off with an init method.