Library, Nanyang Institute of Technology, Nanyang, Henan 473000, China

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Abstract—Frequent itemset mining plays an important part in college library data analysis. Because there are a lot of redundant data in library database, the mining process may generate intra-property frequent itemsets, and this hinders its efficiency significantly. To address this issue, we propose an improved FP-Growth algorithm we call RFP-Growth to avoid generating intra-property frequent itemsets, and to further boost its efficiency, implement its MapReduce version with additional prune strategy. The proposed algorithm was tested using both synthetic and real world library data, and the experimental results showed that the proposed algorithm outperformed existing algorithms.

Index Terms—big data; frequent itemset; data mining; library

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Cite: Xingjian Li, "An Algorithm for Mining Frequent Itemsets from Library Big Data," Journal of Software vol. 9, no. 9, pp. 2361-2365, 2014.