AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

Clustering is an unsupervised learning technique that aims to group similar data points together based on certain characteristics or features. K-Means is a widely used algorithm for this task. It operates by partitioning the dataset into a predefined number of clusters, denoted as 'k'. The algorithm works iteratively, assigning each data point to the nearest cluster centroid, and then recalculating the centroids based on the points assigned to each cluster. This process continues until the centroids stabilize, meaning that assignments of points to clusters no longer change significantly.

K-Means is particularly effective for clustering because it seeks to minimize the variance within each cluster, which leads to more cohesive groups. This attribute makes it a popular choice in various applications, such as market segmentation, social network analysis, and image compression, where grouping similar items is beneficial for further analysis or processing.

In contrast, the other algorithms listed serve different purposes. Linear Regression is used for predicting continuous outcomes based on a linear relationship. Support Vector Machines are primarily focused on classification tasks, aiming to find a hyperplane that best separates different classes in the feature space. Naive Bayes is utilized for classification tasks based on Bayes' theorem and assumes independence among predictors. These characteristics highlight why K-Me