Association Rule Mining Using New FP-Linked List Algorthm

Finding frequent patterns plays a key role in exploring association patterns,correlation, and many other interesting relationships that are applicable in TDB.Several association rule mining algorithms such as Apriori, FP-Growth, and Eclathave been proposed in the literature. FP-Growth algorithm construct a treestructure from transaction database and recursively traverse this tree to extractfrequent patterns which satisfies the minimum support in a depth first searchmanner. Because of its high efficiency, several frequent pattern mining methods andalgorithms have used FP-Growth’s depth first exploration idea to mine frequentpatterns. These algorithms change the FP-tree structure to improve efficiency. Inthis paper, we propose a new frequent pattern mining algorithm based on FP-Growth idea which is using a bit matrix and a linked list structure to extractfrequent patterns. The bit matrix transforms the dataset and prepares it to constructas a linked list which is used by our new FPBitLink Algorithm Our performancestudy and experimental results show that this algorithm outperformed the formeralgorithms.

کلیدواژه ها:

Association Rule Mining ، Support ، frequent pattern ، FP-Growth Algorithm ، itemset