Abstract
Data mining has become an important field and has been applied extensively across many areas. Mining frequent itemsets in a transaction database is critical for mining association rules. Many investigations have estabilished that pattern-growth method outperforms the method of Apriori-like candidate generation. The performance of the pattern-growth method depends on the number of tree nodes. Accordingly, this work presents a new FP-tree structure (NFP-tree) and develops an efficient approach for mining frequent itemsets, based on an NFP-tree, called the NFP-growth approach. NFP-tree employs two counters in a tree node to reduce the number of tree nodes. Additionally, the header table of the NFP-tree is smaller than that of the FP-tree. Therefore, the total number of nodes of all conditional trees can be reduced. Simulation results reveal that the NFP-growth algorithm is superior to the FP-growth algorithm for dense datasets and real datasets.
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Li, YC., Chang, CC. (2004). A New FP-Tree Algorithm for Mining Frequent Itemsets. In: Chi, CH., Lam, KY. (eds) Content Computing. AWCC 2004. Lecture Notes in Computer Science, vol 3309. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30483-8_32
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DOI: https://doi.org/10.1007/978-3-540-30483-8_32
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