We applied the APRIORI algorithm to assess chimpanzee dietary combinations (Agrawal & Srikant, 1994). This method identifies association rules within a large dataset, generating rules with support and confidence exceeding user-specified thresholds (Agrawal & Srikant, 1994; Al-Maolegi & Arkok, 2014). Originally designed for marketing, APRIORI analyzes transaction histories, suggesting additional products to customers (Hahsler, 2017; Hahsler & Karpienko, 2017). Unprecedentedly, we adapted APRIORI to explore nonhuman feeding behavior, providing a fresh approach to testing food resource associations. Merging feeding data from a 4-month period for efficiency, we formatted it akin to the collocation analysis V1 subset. Using the transactions() function from the arules package (Hornik et al., 2005), we transformed the long-form dataset into a Binary Incidence Matrix—ideal for mining associations. Finally, the dataset underwent APRIORI analysis in R (version 4.3.2, R Development Core Team, 2024).

Understanding the results hinges on three customizable metrics: support, confidence, and lift (see Supporting Information S1: Figure 1). Support quantifies the frequency of the association, acting as a popularity metric. In diverse datasets like ours, support tends to be low due to the multitude of item-types. Confidence, scaled between 0 and 1, reflects association strength, with 0 as 0% and 1 as 100%. However, it can be influenced by dataset size; for instance, a rare combination may yield a high confidence. To mitigate this, we turn to the crucial Lift metric, which controls for confidence, especially in smaller datasets. A lift >1 indicates a confidence value exceeding the expected, suggesting a non-random association. This metric proves invaluable in scenarios of low frequency and short data collection spans. Lift, a key indicator, indirectly addresses factors like data collection duration. It is particularly useful in larger datasets with sparse observations for each item or combination. The rule of thumb: Lift should be >1 for confidence to be considered a reliable metric.

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Al-Maolegi, M., & Arkok, B. (2014). An improved Apriori algorithm for association rules. ArXiv Preprint ArXiv:1403.3948.

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