A multi-algorithm clustering framework to optimize plant-knowledge pattern detection in ethnobotanical research

This framework enhances the analytical toolkit for ethnobotanical research. Together, these methods allow understanding how knowledge is structured, shared and specialized within communities. Importantly, the suitability of each algorithm depends on the research context. In this study, binary methods captured broad patterns, partition-based reflected socioeconomic variation, density-based identified specialist profiles, and model-based revealed balanced typologies. Overall, these results provide a basis for understanding and comparing knowledge structures within communities.