expedition clustering
Using unsupervised learning techniques to recreate expedition clusters from archival museum collections data
The Expedition Clustering project aims to analyze and organize botanical specimen collection data by identifying and grouping individual specimens into their respective expeditions. While our dataset contains extensive information on over a million specimens, their association with specific collection expeditions is often unclear. Understanding these connections will improve data organization, enable better summarization, and support the development of interactive, narrative-based tools for both scientists and educators.
By clustering specimens based on collection patterns, locations, collector information, and dates, this project will provide deeper insights into historical and modern botanical expeditions. These insights will facilitate research, highlight under-sampled regions, and help build engaging visualizations for storytelling and scientific analysis.