Association Rule Learning
Definition of association rule learning
Association rule learning is a technique used to discover underlying relationships in a large repository of information found in a relational database. It is used mostly by businesses to determine the relationships between their sales, supplies, employees and clientele among other things. Most association rules are made in formulaic or statement form. For instance if a client purchases eggs and bread, they are 80% likely to purchase milk as well.
In association rule learning, association rules are usually split into two:
- An antecedent (if) which is found in the data
- A consequent (then) which is found after combining the antecedent with a particular item
Useful concepts in Association Rule Learning
The association rules are created through analysis of data in order to determine frequent “if/then” relationships. There are some vital concepts that are normally used to determine the most important frequent relationships:
- Support – this is an indicator of the frequency with which particular items appear within the repository or relational database. It measures the significance of a particular data set or variable. It also helps to eliminate or prune the items that appear infrequently from the database and hence narrow down the variable-relationship search. The main downside of this concept is that it can prune out important items from the data set especially if they appear less frequently.
- Confidence – this is an indicator of the number of times the “if/then” relationships deduced have been confirmed as true. It is also called strength and data derived from support calculations is used to determine the confidence. The downside of confidence as a concept is that it is calculated in such a way that any consequents which have support levels will automatically have high confidence regardless of whether the items involved have minimum or maximum association.
- Conviction – this is a concept that was developed with the aim of accurately capturing the directions of associations between items. Since confidence failed to take into account the accuracy of the associations, the conviction would do so by taking into account information regarding the absence of the consequent.
- Leverage – this concept measures the difference of the units appearing together in order to determine which independent variables can still be categorized together as an antecedent and consequent.
- Lift – this concept determines the probability that two variables would co-occur even if they were independent statistically.
Importance of association rule learning
Association rule learning is a subject that has been extensively researched and written about. It is important in different fields and has been used in the following areas:
- Businesses use association rules to predict customer behavior and shopping patterns.
- Association rules are used in shopping basket data analysis which is commonly done by supermarkets and chain stores.
- It also helps in product clustering and positioning especially for products that have high association levels.
- Association rules are used in designing catalogs in accordance with the preferences and tastes of a targeted clientele.
- These rules can also be used in store layouts and designs.
- In programming, the association rules are used in building programs that have machine learning abilities.
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