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Knowledge and Information Systems

, Volume 28, Issue 2, pp 423–447 | Cite as

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

  • Yi Jia
  • Jintao Zhang
  • Jun HuanEmail author
Regular Paper

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.

Keywords

Graph mining Approximate subgraph isomorphism 

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Electrical Engineering & Computer ScienceUniversity of KansasLawrenceUSA
  2. 2.Center for Bioinformatics, Department of Molecular BiosciencesThe University of KansasLawrenceUSA

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