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

Clusters being non-overlapping means that each sample is assigned to one and only one cluster. This characteristic ensures that there is a clear categorization where distinct groups are formed without any sample being part of multiple clusters at the same time. This principle is fundamental for many clustering algorithms, such as k-means, as it simplifies the data structure, allowing for more straightforward interpretation and analysis of the results.

In this context, non-overlapping clusters facilitate the identification of unique groups within the dataset, which is crucial in applications where each group must be distinctly characterized, such as market segmentation or anomaly detection. By ensuring that each sample belongs to just one cluster, the analysis becomes cleaner and more manageable, enabling clearer insights into the relationships within the data.

The other options do not align with the definition of non-overlapping clusters. Sharing samples or depicting hierarchical structures indicate relationships among clusters that contradict the notion of strict non-overlapping assignments. Similarly, the ability to change cluster sizes dynamically does not pertain to the overlap or exclusivity of cluster assignments. Thus, the idea of non-overlapping clusters ultimately hinges on the exclusivity of sample membership to distinct clusters.