Bloom filter based frequent patterns mining over data streams

JunShan Tan; Zhufang Kuang; Guogui Yang

doi:10.1117/12.2012477

14 March 2013 Bloom filter based frequent patterns mining over data streams

JunShan Tan, Zhufang Kuang, Guogui Yang

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

Citation Download Citation

JunShan Tan, Zhufang Kuang, and 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); https://doi.org/10.1117/12.2012477

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