Department of Computer Engineering, Semnan Branch, Islamic Azad University, Semnan ,Iran
Abstract
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.
Sohrabi, M., Hasannejad Marzooni, H. (2016). Association Rule Mining Using New FP-Linked List Algorithm. Journal of Advances in Computer Research, 7(1), 23-34.
MLA
Mohammad Karim Sohrabi; Hamidreza Hasannejad Marzooni. "Association Rule Mining Using New FP-Linked List Algorithm". Journal of Advances in Computer Research, 7, 1, 2016, 23-34.
HARVARD
Sohrabi, M., Hasannejad Marzooni, H. (2016). 'Association Rule Mining Using New FP-Linked List Algorithm', Journal of Advances in Computer Research, 7(1), pp. 23-34.
VANCOUVER
Sohrabi, M., Hasannejad Marzooni, H. Association Rule Mining Using New FP-Linked List Algorithm. Journal of Advances in Computer Research, 2016; 7(1): 23-34.
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