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

Sebastián Cordero1

  • 1Instituto de Biología, Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile. sebastian.cordero@pucv.cl.

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This study introduces a multi-algorithm framework to analyze ethnobotanical knowledge, revealing its complex structure and heterogeneity within communities. Different algorithms capture distinct knowledge dimensions, enhancing pattern detection and understanding of cultural knowledge variation.

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Area of Science:

  • Ethnobotany
  • Cultural Anthropology
  • Data Science

Background:

  • Ethnobotanical research increasingly uses quantitative methods to identify knowledge patterns.
  • Existing approaches often overlook the multidimensional nature and internal heterogeneity of ethnobotanical knowledge systems within communities.
  • A comprehensive methodological framework is needed to enhance the detection of ethnobotanical knowledge patterns and provide guidance on algorithm selection.

Purpose of the Study:

  • To introduce and evaluate a comprehensive methodological framework based on a multi-algorithm approach for ethnobotanical knowledge pattern detection.
  • To enhance the understanding of the multidimensional nature and internal heterogeneity of ethnobotanical knowledge systems.
  • To provide protocols for algorithm selection tailored to specific research contexts in ethnobotany.

Main Methods:

  • Analysis of an ethnobotanical dataset comprising 1,000 informants from Valparaíso, Chile.
  • Evaluation of five clustering algorithm categories: hierarchical, partition-based, density-based, model-based, and neural network-based.
  • Assessment of algorithm performance using internal validation metrics, cross-method concordance, cluster stability, and novel indices (Variable Influence, Cluster Cohesion, Categorical Homogeneity).

Main Results:

  • Ethnobotanical knowledge exhibits a hierarchical and multidimensional structure, organized from broad community patterns to specialized profiles.
  • Different algorithm types captured distinct dimensions of knowledge variation: hierarchical/partitioning methods identified broad patterns, density-based/neural models detected specialized profiles, and model-based methods revealed balanced structures.
  • Age and occupation were key sociodemographic predictors of knowledge organization, with low concordance among algorithms highlighting their complementary roles in capturing cultural knowledge variation.

Conclusions:

  • The proposed multi-algorithm framework significantly enhances the analytical toolkit for ethnobotanical research.
  • This approach facilitates a deeper understanding of how ethnobotanical knowledge is structured, shared, and specialized within communities.
  • Algorithm suitability is context-dependent, with different methods revealing specific aspects of knowledge structures, enabling better comparison across communities.