Diversity-Based Sampling Strategies: Leveraging Unsupervised Techniques

Diversity-based sampling strategies are predicated upon the efficacy of unsupervised techniques, notably clustering, in achieving their objectives. These strategies aim to capture the diversity within a population by selecting samples that are representative of different subgroups or clusters. The effectiveness of such strategies hinges on the ability of unsupervised techniques, such as clustering, to accurately identify and delineate these diverse subgroups. By employing clustering algorithms, diversity-based sampling methods can identify distinct clusters within the population, allowing for the selection of representative samples from each cluster. This approach ensures that the resulting sample is not only diverse but also captures the full spectrum of characteristics present in the population.