Exclusive Clustering
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What is Exclusive Clustering?
Exclusive clustering, also known as hard clustering, is a type of clustering where each data point can only belong to one cluster. This means there's no overlap between clusters; a data point is definitively assigned to a single group. The goal is to partition the data into distinct, non-overlapping clusters based on similarity. For example, K-Means is an exclusive clustering algorithm. It aims to partition n data points into k clusters in which each data point belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.
Free resources to learn Exclusive Clustering
- ArticleK-means | scikit-learn
- ArticleUnsupervised Clustering: A Guide
More in stage 23: What is Reinforcement Learning?
Exclusive Clustering: common questions
Can I learn Exclusive 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 Exclusive 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 Exclusive Clustering tutorial for beginners?
It is lesson 91 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.