Abstract
Parallel association rules mining is a high performance mining method. Until now there are many parallel algorithms to mine association rules, this paper emphatically analyses existing parallel mining algorithms’ realization skill and defects. On the basis, a new data structure, called FP-Forest, is designed with a multi-trees structure to store data. At the same time, a new parallel mining model is proposed according to the property of FP-Forest, which combines the advantage of data-parallel method and task-parallel method. First, database is reasonably divided to data processing nodes by core processor, and FP-Forest structure is built on data processing nodes for each sub-database. Secondly, core node perform a one-time synchronization merging for each FP-Forest, and every MFP-Tree on FP-Forest is dynamical assigned to corresponding mining node as sub-task by task-parallel technique. Furthermore, a fast parallel mining algorithm, namely F-FDPM, is presented to mine association rules according to above model, which mining process adopts frequent growth method basing on deepth-first searching strategy. From experimentation on real data sets, the algorithm has greatly enhanced association rules mining efficiency.
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Hu, J., Yang-Li, X. (2008). A Fast Parallel Association Rules Mining Algorithm Based on FP-Forest. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_5
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DOI: https://doi.org/10.1007/978-3-540-87734-9_5
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