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.
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Codes and datasets available at: http://fimi.cs.helsinki.fi/
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ER (2004): http://www.cs.fudan.edu.cn/er2004/
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Yan, Y., Li, Z., Wang, T., Chen, Y., Chen, H. (2004). Mining Maximal Frequent ItemSets Using Combined FP-Tree. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_42
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DOI: https://doi.org/10.1007/978-3-540-30549-1_42
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