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How k-means clustering is different from knn?
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Answer / Raj Kapoor Agrahari
K-means clustering and k-nearest neighbors (kNN) are two distinct machine learning algorithms. K-means is an unsupervised learning algorithm used for grouping data into clusters, while kNN is a supervised learning algorithm used for classification or regression tasks. In k-means, the goal is to find the optimal number of clusters (k), and assign each data point to one of those clusters based on distance, whereas in kNN, the goal is to predict the label of an unseen sample by finding its k nearest neighbors in the training data.
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