Probabilistic (Fuzzy) Clustering: Each observation is assigned to one or more clusters with a probability of belonging to each. The cluster assignments are conceptualized as having a probability distribution of belonging to each cluster rather than one exclusive assignment. This has the advantage of quantifying uncertainty if there is ambiguity in cluster assignments. There exists a fuzzy version of K-Means that implements soft clustering, and Expectation-Maximization approaches like Gaussian Mixture models also have this capability.
What is Probabilistic (Fuzzy) Clustering?
- Categories: Clustering
- Updated: March 26, 2023
Help us improve this post by suggesting in comments below: - modifications to the text, and infographics
- video resources that offer clear explanations for this question
- code snippets and case studies relevant to this concept
- online blogs, and research publications that are a “must read” on this topic
- video resources that offer clear explanations for this question
- code snippets and case studies relevant to this concept
- online blogs, and research publications that are a “must read” on this topic
Partner Ad
- Other Questions in Clustering
Find out all the ways
that you can