The Role of Unsupervised Techniques in Diversity-Based Sampling Strategies

Diversity-based sampling strategies are heavily reliant on the influence of unsupervised techniques, such as clustering. These strategies aim to ensure that the collected data represents the full diversity present within a population. Unsupervised techniques, particularly clustering, play a vital role in identifying and grouping data points with similar characteristics, enabling the selection of diverse samples that accurately reflect the underlying population structure.