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K-Means Clustering Explained by Gaynika Sharma is a document available to read on EtoBox.
What is K-Means Clustering Explained about?
The K-Means Clustering Algorithm is an unsupervised learning method used to group unlabeled data into predefined clusters based on similarity. It operates iteratively by assigning data points to the nearest cluster centroid and recalculating centroids until convergence. While it is efficient and easy to interpret, it requires prior knowledge of the number of clusters and may not yield globally optimal results.
- Author
- Gaynika Sharma
- Language
- EN