Publicly collected information on artificial intelligence (AI) tools provides monitoring data, but relationships among their attributes remain insufficiently explored. This study compared Apriori, FP-Growth, and ECLAT to identify a computationally suitable association rule mining algorithm for future integration into the researcherdeveloped AI Compass system. A dataset of 291 records collected from February 5 to October 15, 2026, was transformed into 291 transactions containing 184 unique items. The algorithms were evaluated using identical support, confidence, and lift thresholds, with execution time, traced Python memory allocation, rule consistency, and support sensitivity examined. All three algorithms generated 41 frequent itemsets and 40 association rules. Apriori achieved the shortest median mining runtime of 2.9801 ms, while FP-Growth recorded the lowest peak traced memory allocation of 0.0879 MiB. The discovered rules revealed associations among AI tool attributes. Apriori was identified as the preferred candidate when execution time was prioritized, with rule artifacts saved for future deployment.