IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

The algorithm K-means clustering is specifically designed for clustering analysis, which involves grouping a set of data points into clusters based on their similarity. This method works by partitioning the data into a predetermined number of clusters. The algorithm iteratively assigns each data point to one of the clusters based on the nearest centroid, which is the average position of all the points in that cluster. K-means is popular due to its simplicity and efficiency, particularly with large datasets.

In contrast, linear regression focuses on predicting a numeric outcome based on one or more predictor variables, making it unsuitable for clustering tasks. Decision trees are primarily used for classification and regression tasks, providing a way to model decision-making processes rather than grouping data points. Support vector machines are most commonly employed for classification tasks, finding hyperplanes that best separate different classes, and are not intended for clustering.

Thus, K-means clustering is the only algorithm listed that is explicitly designed for clustering analysis, which underscores its selection as the correct answer.