Mining maximal frequent itemsets using combined FP-Tree

Authors:
Yuejin Yan;Zhoujun Li;Tao Wang;Yuexin Chen;Huowang Chen
Affiliations:
School of Computer Science, National University of Defense Technology, Changsha, China;School of Computer Science, National University of Defense Technology, Changsha, China;School of Computer Science, National University of Defense Technology, Changsha, China;School of Computer Science, National University of Defense Technology, Changsha, China;School of Computer Science, National University of Defense Technology, Changsha, China
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

Maximal frequent itemsets mining is one of the most fundamental problems in data mining In this paper, we present CfpMfi, a new depth-first search algorithm based on CFP-tree for mining MFI Based on the new data structure CFP-tree, which is a combination of FP-tree and MFI-tree, CfpMfi takes a variety pruning techniques and a novel item ordering policy to reduce the search space efficiently Experimental comparison with previous work reveals that, on dense datasets, CfpMfi prunes the search space efficiently and is better than other MFI Mining algorithms on dense datasets, and uses less main memory than similar algorithm.