Abstract
As far as we know, a little research of global maximum frequent itemsets had been done. Therefore, a fast mining algorithm of global maximum frequent itemsets was proposed, namely, FMAGMFI algorithm. Firstly, each computer nodes compute local maximum frequent itemsets with DMFIA algorithm and local FP-tree. Secondly, the center node combined local maximum frequent itemsets. Finally, global maximum frequent itemsets were gained by the searching strategy of top-bottom. Adopting FP-tree structure, FMAGMFI algorithm greatly reduces runtime compared with Apriori-like algorithms. Theoretical analysis and experimental results suggest that FMAGMFI algorithm is efficient.
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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He, B. (2012). Fast Mining Algorithm of Global Maximum Frequent Itemsets. In: Xie, A., Huang, X. (eds) Advances in Electrical Engineering and Automation. Advances in Intelligent and Soft Computing, vol 139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27951-5_30
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DOI: https://doi.org/10.1007/978-3-642-27951-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27950-8
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