K-Means Market Basket Analysis
Two-person honours research project on speeding up association rule mining with a K-Means pre-filter that clusters items on revenue and co-purchased revenue and drops the lowest-value cluster before Apriori runs. Built Apriori from scratch in Java (candidate generation, pruning, support counting) as the supporting algorithm that mines rules from the filtered data. Benchmarked the K-Means-filtered pipeline against Apriori on the full dataset and a published clustering method on 130K+ records, with comparable top-100 rule value.
