Abstract
An exemplar sample is a small subset of a dataset that forms a characterization of the dataset and can also be used as a list of targets for further inspection. Traditionally exemplars are encountered in cluster analysis techniques such as k-medoids, where each exemplar represents a different cluster. This article extends the concept of exemplar sampling by constructing four criteria for choosing a sample: similarity, distinctness, exhaustiveness, and typicalness. Each criterion illustrates a different way of characterizing the dataset. We describe an adapted version of the CLARANS algorithm which can be used to efficiently draw exemplar samples, and show some examples on test datasets. Exemplar sampling is proposed for use in finite population inference, as an alternative to methods such as stratification or balancing.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
