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Exercise 3_​1 - Marketbasket Analysis

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

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

Market Basket Analysis: Steps for Building Association Rules 1. Read Transaction/Basket data and Product data 2. Using an association rule algorithm to build a product association rule set 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 Questions:1. What can the retailer do with the recommended productinformation?2. How many times in the first 100 rowsin the Row Filternode report is Blueberry the recommended item?3. Limes are associated with what vegetable antecedent? calculate association rules("A priori" algorithm)- translate antecedent collectionto product name concatenations- associate consequent product ID to product name- calculate price stats and rule revenueenter min supportNode 120ItemsTransNode 125Replaces space delimiter with comma Association RuleLearner (Borgelt) Ungroup collections Double Input Row Filter CSV Reader CSV Reader Number To String Cell Splitter Market Basket Analysis: Steps for Building Association Rules 1. Read Transaction/Basket data and Product data 2. Using an association rule algorithm to build a product association rule set 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 Questions:1. What can the retailer do with the recommended productinformation?2. How many times in the first 100 rowsin the Row Filternode report is Blueberry the recommended item?3. Limes are associated with what vegetable antecedent? calculate association rules("A priori" algorithm)- translate antecedent collectionto product name concatenations- associate consequent product ID to product name- calculate price stats and rule revenueenter min supportNode 120ItemsTransNode 125Replaces space delimiter with commaAssociation RuleLearner (Borgelt) Ungroup collections Double Input Row Filter CSV Reader CSV Reader Number To String Cell Splitter

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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: 2020-08-07
Last update: 2024-12-17
Created with KNIME version: v4.2.0
Tags: retail association rules recommandation engine market basket analysis MBA Apriori algorithm Borgelt data collection Quickform

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