Abstract
Mining frequent subtree patterns in a tree database (or, forest) is useful in domains such as bioinformatics and mining semi-structured data. We consider the problem of mining embedded subtrees in a database of rooted, labeled, and ordered trees. We compare two existing serial mining algorithms, PrefixTreeSpan and TreeMiner, and adapt them for parallel execution using PrefixFPM, our general-purpose framework for frequent pattern mining that is designed to effectively utilize the CPU cores in a multicore machine. Our experiments show that TreeMiner is faster than its successor PrefixTreeSpan when a limited number of CPU cores are used, as the total mining workloads is smaller; however, PrefixTreeSpan has a much higher speedup ratio and can beat TreeMiner when given enough CPU cores.
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Acknowledgments
This work was partially supported by NSF OAC-1755464 and DGE-1723250.
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Qu, W., Yan, D., Guo, G., Wang, X., Zou, L., Zhou, Y. (2020). Parallel Mining of Frequent Subtree Patterns. In: Qin, L., et al. Software Foundations for Data Interoperability and Large Scale Graph Data Analytics. SFDI LSGDA 2020 2020. Communications in Computer and Information Science, vol 1281. Springer, Cham. https://doi.org/10.1007/978-3-030-61133-0_2
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