# Support Thresholds, Not Algorithms, Limit Rare-Association Recovery in Co-Purchase Networks

arXiv: https://arxiv.org/abs/2609.20171 · HTML: https://mlsanity.com/paper/2609.20171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Network Backboning with Noisy Data](https://mlsanity.com/paper/1701.07336/) (2017)
- [CATCH: A Modular Cross-domain Adaptive Template with Hook](https://mlsanity.com/paper/2510.26582/) (2025)
- [Extracting Complements and Substitutes from Sales Data: A Network Perspective](https://mlsanity.com/paper/2103.02042/) (2021)
- [An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks](https://mlsanity.com/paper/2503.16350/) (2025)
- [backbone: An R package to extract network backbones](https://mlsanity.com/paper/2203.11055/) (2022)
- [Market Basket Analysis Using Rule-Based Algorithms and Data Mining Techniques](https://mlsanity.com/paper/2412.18699/) (2024)

## Built on by (most cited first, 1 of 1)

- [What Breaks in Multi-Image Schema Application? Factorial Oracle Interventions Reveal an Instance-Information Bottleneck](https://mlsanity.com/paper/2610.19611/) (2026)

## Abstract (arXiv)

The support threshold of the Apriori algorithm involves a trade-off in conducting market basket analysis: the associations that occur frequently are noted with high threshold; however, the low ones lead to generating the large amount of rules. The paper compares five methods for co-purchase edge filtration on two grocery datasets: i.e., Instacart (3.2 million baskets) and Dunnhumby (208 thousand baskets), including Apriori, Apriori + lift post-filtering, top-K ranking based on lift, and two methods based on networks, noise-corrected (NC) and disparity filter (DF). The top-K method ensures the maximum average lift, while the NC achieves similar lift level by means of a single value of the significance parameter (α). These two methods recover substantially more rare high-lift associations than Apriori (80-100% against 22-28%). NC and top-K select meaningfully different edges (18-29% non-overlapping): NC retains statistically validated pairs, while top-K retains rare pairs with high lift but low statistical significance. A rolling-origin holdout evaluation shows that top-K edges recur at higher rates at every split, but NC edges are ~12 pp more likely to remain statistically significant in the held-out network.