14 March 2013 Bloom filter based frequent patterns mining over data streams
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Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 87685V (2013) https://doi.org/10.1117/12.2012477
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore
Abstract
A data streams synopses structure FPCBF, which is based on bloom filter, is proposed in this paper. Each unit of vector BF[e] in FPCBF is a two-tuples. The transactions insert operation (InsertTran) and 1-frequent patterns pregeneration operation (PreGenFP) is defined. Then, A data streams frequent patterns mining algorithm BFFPM which is based on FPCBF is proposed in this paper. The BFFPM algorithm contains two parts: 1-frequent patterns generation and r-frequent patterns generation. The problem of computing the 1-frequent patterns generation is transformed into the problem of computing the longest common sub-sequence α i LCS of k setting identifier sequence in BF[e] in FPCBF. In the same way, the problem of computing the r-frequent patterns generation is transformed into the problem of computing the longest common sub-sequence LCS r , which is the longest common sequence of the identifier sequence α i LCS ,…, i r LCS α + of r items. The IBM synthesizes data generation which output customers shopping a data are adopted as experiment data. The FPCBF algorithm not only has high precision for mining frequent patterns, but also has low memory requirement
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JunShan Tan, JunShan Tan, Zhufang Kuang, Zhufang Kuang, Guogui Yang, Guogui Yang, } "Bloom filter based frequent patterns mining over data streams", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87685V (14 March 2013); doi: 10.1117/12.2012477; https://doi.org/10.1117/12.2012477
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