Association Rule Mining Using New FP-Linked List Algorithm

Abstract

<em>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 Eclat have been proposed in the literature. FP-Growth algorithm construct a tree structure from transaction database and recursively traverse this tree to extract frequent patterns which satisfies the minimum support in a depth first search manner. Because of its high efficiency, several frequent pattern mining methods and algorithms have used FP-Growth's depth first exploration idea to mine frequent patterns. These algorithms change the FP-tree structure to improve efficiency. In this 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 extract frequent patterns. The bit matrix transforms the dataset and prepares it to construct as a linked list which is used by our new FPBitLink Algorithm. Our performance study and experimental results show that this algorithm outperformed the former algorithms.</em>