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An efficient graph-mining method for complicated and noisy data with real-world applications

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Abstract

In this paper, we present a novel graph database-mining method called APGM (APproximate Graph Mining) to mine useful patterns from noisy graph database. In our method, we designed a general framework for modeling noisy distribution using a probability matrix and devised an efficient algorithm to identify approximate matched frequent subgraphs. We have used APGM to both synthetic data set and real-world data sets on protein structure pattern identification and structure classification. Our experimental study demonstrates the efficiency and efficacy of the proposed method.

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Authors and Affiliations

  1. Department of Electrical Engineering & Computer Science, University of Kansas, Lawrence, KS, 66045, USA

    Yi Jia & Jun Huan

  2. Center for Bioinformatics, Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, 66046, USA

    Jintao Zhang

Authors
  1. Yi Jia
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  2. Jintao Zhang
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  3. Jun Huan
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Correspondence to Jun Huan.

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Jia, Y., Zhang, J. & Huan, J. An efficient graph-mining method for complicated and noisy data with real-world applications. Knowl Inf Syst 28, 423–447 (2011). https://doi.org/10.1007/s10115-010-0376-y

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  • DOI: https://doi.org/10.1007/s10115-010-0376-y

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