Análise de Cesta de Mercado com Algorítimo

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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

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

Nodes

• Column Rename2 ×
• Joiner2 ×
• Table Reader2 ×
• Association Rule Learner (Borgelt)1 ×
• Cell Splitter1 ×
• Show all 12 nodes

Extensions

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

Links

• No links available