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Market Basket Analysis: Building Association Rules

This workflow builds a recommandation engine for market basket analysis using the Borgelt version of the Apriori algorithm.

URL: Market Basket Analysis and Recommandation Engines https://www.knime.org/knime-applications/market-basket-analysis-and-recommendation-engines
URL: Market Basket Analysis and Recommandation Engines https://www.knime.org/blog/market-basket-analysis-and-recommendation-engines

Exercise: Market Basket Analysis: Build Association Rules This workflow reads transaction/basket data and product data. The goal is to extract association rules based on the transaction/basket data.1. Use the association Rule Learner (Borgelt) node to calculate the association rules with the following settings: - min. set size = 1 - min rule confidence = 10% - min support 2.52. Translate the product IDs in the antecedent collection and the consequence to product names by appliying the "Translate Product Ids to Product Names" metanode.
Read data Top: Transactions Bottom: Products
Read Historical Basket Data
Top: Association rulesBottom: Lookup table Products and Product Ids
Translate Product Ids to Product Names
Association Rule Learner (Borgelt)
write association rules to a file
Table Writer (deprecated)

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: rosy
Created at: 2015-03-23
On NodePit since: 2025-10-30
Last update: 2025-10-30
Created with KNIME version: v5.8.0
Tags: retailassociation rulesrecommandation enginemarket basket analysisMBAApriori algorithmBorgeltdata collectionQuickformsupply chainretailrecommendation engine

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