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  • 01_Build_Association_Rules_for_MarketBasketAnalysis
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Market Basket Analysis: Building Association Rules

Retail Association rules Recommandation engine Market basket analysis MBA
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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

External resources

  • Market Basket Analysis and Recommandation Engines
  • Market Basket Analysis and Recommandation Engines

Used extensions & nodes

Created with KNIME Analytics Platform version 4.6.1
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.1

    knime
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    KNIME Itemset Mining Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
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  2. Go to item
  3. Go to item
  4. Go to item
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  6. Go to item
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