Probabilistic Clustering
Last updated
What is Probabilistic Clustering?
Probabilistic clustering assumes that the data is generated from a mixture of probability distributions. Instead of assigning each data point to a single cluster, it provides the probability of a data point belonging to each cluster. A common example is the Gaussian Mixture Model (GMM), where it's assumed that the data points are generated from a mixture of Gaussian distributions. Scikit-learn provides an implementation of GMM that can be used for probabilistic clustering.
Free resources to learn Probabilistic Clustering
- ArticleGaussian mixture models
- ArticleGaussian Mixture Model Explained
More in stage 23: What is Reinforcement Learning?
Probabilistic Clustering: common questions
Can I learn Probabilistic Clustering in Hindi?
This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain Probabilistic Clustering in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.
Is this Probabilistic Clustering tutorial for beginners?
It is lesson 94 of 150 in the free Machine Learning roadmap, in stage 23, What is Reinforcement Learning?. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.