Mining Maximal Patterns Based on Improved FP-tree and Array Technique

• Published in: 2010 Third International Symposium on Intelligent Information Technology and Security Informatics pp. 567 - 571
• Main Authors: Hua-jin Wang, Chun-an Hu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2010
• Subjects: Array technique; Association rules; Data mining; Data security; Frequency; Improved FP-tree; Informatics; Information security; Information technology; Itemsets; Maximal frequent pattern; Testing; Transaction databases
• ISBN: 9781424467303; 1424467306
• DOI: 10.1109/IITSI.2010.185

Mining frequent patterns is important for mining association rules. However, because of the inherent complexity, mining complete frequent patterns from a dense database could be impractical, and the quantity of the mined patterns is usually very large, it is hard to understand and make use of them. Maximal frequent patterns contain and compress all frequent patterns, and the memory needed for saving them is much smaller than that needed for saving complete patterns, thus it is greatly valuable to mine maximal frequent patterns. In this paper, the structure of a traditional FP-tree is improved , an efficient algorithm for mining maximal frequent patterns based on improved FP-tree and array technique, called IAFP-max, is presented. By introducing the concept of postfix sub-tree, the presented algorithm needn't generate the candidate of maximal frequent patterns in mining process and therefore greatly reduces the memory consume, and it also uses an array-based technique to reduce the traverse time to the improved FP-tree. The experimental evaluation shows that this algorithm outperforms most exiting algorithms MAFIA, GenMax and FPmax*.