Abstract
Frequent pattern mining has become an important data mining task and has been a focused theme in data mining research. Frequent patterns are patterns that appear in a data set frequently. Frequent pattern mining searches for recurring relationship in a given data set. Various techniques have been proposed to improve the performance of frequent pattern mining algorithms. This paper presents review of different frequent mining techniques including apriori based algorithms, partition based algorithms, DFS and hybrid algorithms, pattern based algorithms, SQL based algorithms and Incremental apriori based algorithms. A brief description of each technique has been provided. In the last, different frequent pattern mining techniques are compared based on various parameters of importance. Experimental results show that FP- Tree based approach achieves better performance.
Highlights
Frequent patterns are item sets, subsequences, or substructures that appear in a data set with frequency no less than a user-specified threshold
Frequent pattern mining is a first step in association rule mining
When the number of candidate frequent item sets is relatively large, the hybrid algorithm switches to transaction identification (TID) set intersection with depth first search (DFS), since simple TID set intersection is more efficient than occurrence counting when the number of candidate frequent item sets is relatively large
Summary
Frequent patterns are item sets, subsequences, or substructures that appear in a data set with frequency no less than a user-specified threshold. An item set is considered as frequent or large, if the item set has a support that is greater or equal to the user specified minimum support [25,26]. The support of the rule X Y is equal to the percentage of transactions in D containing X Y. The confidence of the rule X Y is equal to the percentage of transactions in D containing X containing Y. The problem can be further broken down into two steps: mining of frequent item sets and generating association rules. The first strategy is to count the occurrences directly, whenever an item set is contained in a transaction, the occurrence of the item set is increased. The second strategy is to count the occurrences indirectly by intersecting TID set of each component of the item set.
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