[amp_mcq option1=”k-means clustering is a method of vector quantization” option2=”k-means clustering aims to partition n observations into k clusters” option3=”k-nearest neighbor is same as k-means” option4=”none of the mentioned” correct=”option3″]
The correct answer is C. k-nearest neighbor is not the same as k-means.
K-means clustering is a method of vector quantization, which is a technique that aims to represent a set of data points with a smaller number of points. K-means clustering does this by partitioning the data points into k clusters, such that each data point is assigned to the cluster with the nearest mean.
K-nearest neighbor is a supervised machine learning algorithm that can be used for classification or regression. In k-nearest neighbor, each data point is classified or predicted by taking the majority vote of its k nearest neighbors.
K-means clustering and k-nearest neighbor are both popular machine learning algorithms, but they have different purposes and work in different ways.