Answer / Raj Bhushan Dube

K-nearest neighbors (knn) and k-means clustering are two popular machine learning algorithms used for data classification and clustering, but they differ significantly in their approach. k-means clustering is an unsupervised learning algorithm that groups similar data points together based on the Euclidean distance. It assigns each data point to one of k clusters by minimizing the sum of squared distances between a data point and its assigned cluster's centroid. On the other hand, knn is a supervised learning algorithm that classifies new data points based on the majority vote of their k-nearest neighbors in the training dataset. Unlike k-means, it does not require predefined clusters.

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