What is Frequent Pattern Mining?

What is Frequent Pattern Mining?
1.     Frequent pattern mining searches for recurring relationships in a given data set.
2.     Frequent itemset mining leads to the discovery of associations and correlations among items in large transactional or relational data sets.
3.     The discovery of interesting correlation relationships among huge amounts of business transaction records can help in many business decision-making processes, such as catalog design, cross-marketing, and customer shopping behavior analysis.
4.     Market basket analysis is just one form of frequent pattern mining.
5.     Classification of Frequent pattern mining:
·       Based on the completeness of patterns to be mined:
o   We can mine the complete set of frequent itemsets, the closed frequent itemsets, and the maximal frequent itemsets, given a minimum support threshold.We can also mine constrained frequent itemsets ,approximate frequent itemsets , near-match frequent itemsets , top-k frequent itemsets.
o   Different applications may have different requirements regarding the completeness of the patterns to be mined, which in turn can lead to different evaluation and optimization methods.
·       Based on the levels of abstraction involved in the rule set: 
o   Some methods for association rule mining can find rules at differing levels of abstraction.
o   For example,
§  buys(X, “computer”)buys(X, HP printer)
§  buys(X, “laptop computer”)buys(X, HP printer)
o   In these Rules, the items bought are referenced at different levels of abstraction.
o   The rule set mined is consisting of multilevel association rules. If, instead, the rules within a given set do not reference items or attributes at different levels of abstraction, then the set contains single-level association rules.
·       Based on the number of data dimensions involved in the rule: 
o   If the items or attributes in an association rule reference only one dimension, then it is a single-dimensional association rule
o   For example,
§  buys(X, “computer”)buys(X, antivirus software)
o   If a rule references two or more dimensions, then it is a multidimensional association rule. For example,
§  age(X, “30…39”)^income(X, “42K…48K”)buys(X, high resolution TV):
·       Based on the types of values handled in the rule:
o    If a rule involves associations between the presence or absence of items, it is a Boolean association rule.
o    For example,
§  Rules buys(X, “computer”))buys(X, “HP printer”) are Boolean association rules.
o   If a rule describes associations between quantitative items or attributes, then it is a quantitative association rule.
§  age(X, “30…39”)^income(X, “42K…48K”)buys(X, high resolution TV):
§  Age and income, have been discretized.
·       Based on the kinds of rules to be mined: 
o   Frequent pattern analysis can generate various kinds of rules and other interesting relationships. Many of the rules are redundant or do not indicate a correlation relationship among itemsets. Thus, the discovered associations can be further analyzed to uncover statistical correlations, leading to correlation rules.
o   We can also mine strong gradient relationships among items. Example “The average sales from Sony Digital Camera increase over 16% when sold together with Sony Laptop Computer”: both Sony Digital Camera and Sony Laptop Computer are siblings, where the parent itemset is Sony.
·       Based on the kinds of patterns to be mined: 
Many kinds of frequent patterns can be mined from different kinds of data sets like frequent itemset mining, Sequential pattern mining, Structured pattern mining.


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