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

1. Read Transaction/Basket data and Product data
2. Using "A priori" algorithm, build association rule set
- min. set size = 1
- min rule confidence = 10%
- min support is controlled by Double Input Quickform node in %
3. Translate Antecedent collections into product name concatenations
4. Translate Consequent Item ID into Consequent Product Name
5. Calculate price stats and rule revenue
6. Write assciation rule set to file

Nodes

• Table Reader3 ×
• Cell Splitter2 ×
• Column Rename2 ×
• GroupBy2 ×
• Joiner2 ×
• Show all 17 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Itemset Mining
• FeatureKNIME Math Expression (JEP)
• FeatureKNIME Quick Forms

Links

• Market Basket Analysis and Recommandation Engines

  www.knime.org/knime-applications/market-basket-analysis-and-recommendation-engines
• Market Basket Analysis and Recommandation Engines

  www.knime.org/blog/market-basket-analysis-and-recommendation-engines
• Urgent!!!!! Error with Association Rule Learner (Borgelt)

  forum.knime.com/p/138471