Web application using shiny to gain association rules using Aproiri algorithm within a transactional database.
1. INTRODUCTION :
Market Basket Analysis : Technique used by large retailers to uncover associations between items . It works by looking for a combinations of items that occurs together frequently in transactions, providing information to understand the purchase behavior.
First it’s important to define the Apriori algorithm, including some statistical concepts (support, confidence, lift and conviction) to select interesting rules.
1. ASSOCIATION RULES :
The Apriori algorithm generates association rules for a given data set. An association rule implies that if an item A occurs, then item B also occurs with a certain probability. Let’s see an example :
| items | |
|---|---|
| [1] | {BISCUIT,BREAD,MAGGI,TEA} |
| [2] | {BREAD,JAM,MAGGI,TEA} |
| [3] | {BREAD,MILK} |
| [4] | {BISCUIT,COCK,COFFEE,CORNFLAKES} |
| [5] | {BOURNVITA,COFFEE,SUGER} |
| [6] | {BREAD,COCK,COFFEE} |
