**Association rule learning** is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.^{[1]}

**Alternative measures of interestingness**

In addition to confidence, other measures of *interestingness* for rules have been proposed. Some popular measures are:

- All-confidence
^{[7]} - Collective strength
^{[8]} - Leverage
^{[9]}

Several more measures are presented and compared by Tan et al.^{[10]} and by Hahsler.^{[4]} Looking for techniques that can model what the user has known (and using these models as interestingness measures) is currently an active research trend under the name of “Subjective Interestingness.”

Statistically sound associations

One limitation of the standard approach to discovering associations is that by searching massive numbers of possible associations to look for collections of items that appear to be associated, there is a large risk of finding many spurious associations. These are collections of items that co-occur with unexpected frequency in the data, but only do so by chance. For example, suppose we are considering a collection of 10,000 items and looking for rules containing two items in the left-hand-side and 1 item in the right-hand-side. There are approximately 1,000,000,000,000 such rules. If we apply a statistical test for independence with a significance level of 0.05 it means there is only a 5% chance of accepting a rule if there is no association. If we assume there are no associations, we should nonetheless expect to find 50,000,000,000 rules. Statistically sound association discovery^{[18][19]} controls this risk, in most cases reducing the risk of finding *any* spurious associations to a user-specified significance level.

Algorithms

Many algorithms for generating association rules have been proposed.

Some well-known algorithms are Apriori, Eclat and FP-Growth, but they only do half the job, since they are algorithms for mining frequent itemsets. Another step needs to be done after to generate rules from frequent itemsets found in a database.

**Apriori algorithm**

Apriori^{[13]} uses a breadth-first search strategy to count the support of itemsets and uses a candidate generation function which exploits the downward closure property of support.

**Eclat algorithm**

Eclat^{[14]} (alt. ECLAT, stands for Equivalence Class Transformation) is a depth-first search algorithm based on set intersection. It is suitable for both sequential as well as parallel execution with locality-enhancing properties.^{[20][21]}

**Others**

**ASSOC**

The ASSOC procedure^{[24]} is a GUHA method which mines for generalized association rules using fast bitstrings operations. The association rules mined by this method are more general than those output by apriori, for example “items” can be connected both with conjunction and disjunctions and the relation between antecedent and consequent of the rule is not restricted to setting minimum support and confidence as in apriori: an arbitrary combination of supported interest measures can be used.

**OPUS search**

OPUS is an efficient algorithm for rule discovery that, in contrast to most alternatives, does not require either monotone or anti-monotone constraints such as minimum support.^{[25]} Initially used to find rules for a fixed consequent^{[25][26]} it has subsequently been extended to find rules with any item as a consequent.^{[27]} OPUS search is the core technology in the popular Magnum Opus association discovery system.

Lore

A famous story about association rule mining is the “beer and diaper” story. A purported survey of behavior of supermarket shoppers discovered that customers (presumably young men) who buy diapers tend also to buy beer. This anecdote became popular as an example of how unexpected association rules might be found from everyday data. There are varying opinions as to how much of the story is true.^{[28]} Daniel Powers says:^{[28]}

In 1992, Thomas Blischok, manager of a retail consulting group at Teradata, and his staff prepared an analysis of 1.2 million market baskets from about 25 Osco Drug stores. Database queries were developed to identify affinities. The analysis “did discover that between 5:00 and 7:00 p.m. that consumers bought beer and diapers”. Osco managers did NOT exploit the beer and diapers relationship by moving the products closer together on the shelves.

Other types of association rule mining

**Multi-Relation Association Rules**: Multi-Relation Association Rules (MRAR) are association rules where each item may have several relations. These relations indicate indirect relationship between the entities. Consider the following MRAR where the first item consists of three relations *live in*, *nearby* and *humid*: “Those who *live in* a place which is *nearby* a city with *humid* climate type and also are *younger* than 20 -> their *health condition* is good”. Such association rules are extractable from RDBMS data or semantic web data.^{[29]}

**Contrast set learning** is a form of associative learning. **Contrast set learners** use rules that differ meaningfully in their distribution across subsets.^{[30][31]}

**Weighted class learning** is another form of associative learning in which weight may be assigned to classes to give focus to a particular issue of concern for the consumer of the data mining results.

**High-order pattern discovery** facilitate the capture of high-order (polythetic) patterns or event associations that are intrinsic to complex real-world data. ^{[32]}

**K-optimal pattern discovery** provides an alternative to the standard approach to association rule learning that requires that each pattern appear frequently in the data.

**Approximate Frequent Itemset** mining is a relaxed version of Frequent Itemset mining that allows some of the items in some of the rows to be 0.^{[33]}

**Generalized Association Rules** hierarchical taxonomy (concept hierarchy)

**Quantitative Association Rules** categorical and quantitative data

**Interval Data Association Rules** e.g. partition the age into 5-year-increment ranged

**Sequential pattern mining **discovers subsequences that are common to more than minsup^{[clarification needed]} sequences in a sequence database, where minsup is set by the user. A sequence is an ordered list of transactions.^{[34]}

**Subspace Clustering**, a specific type of Clustering high-dimensional data, is in many variants also based on the downward-closure property for specific clustering models.^{[35]}

**Warmr **is shipped as part of the ACE data mining suite. It allows association rule learning for first order relational rules.^{[36]}

References

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