Machine Learning Fights Cannibalization in the Retail Industry

Once the association discovery model is created, it's a good idea to monitor how well those association rules perform on a daily basis to constantly learn from the new incoming sales transaction data. Therefore, we also train anomaly detectors, which is a powerful tool to measure the reliability of association rules. We build an anomaly detector every time the association rules are produced. Having quantified how anomalous the new daily sales transaction data distribution is, we can get a sense of how different the new data is from the data that was used to produce the original association rules (see Figure 5). This approach tells Machine Learning analysts when to retrain the association rules.