association rule mining using new fp-linked list algorithm

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.