Discovering frequent graph patterns using disjoint paths

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

Whereas data mining in structured data focuses on frequent data values, in semistructured and graph data mining, the issue is frequent labels and common specific topologies. Here, the structure of the data is just as important as its content. We study the problem of discovering typical patterns of graph data, a task made difficult because of the complexity of required subtasks, especially subgraph isomorphism. In this paper, we propose a new Apriori-based algorithm for mining graph data, where the basic building blocks are relatively large, disjoint paths. The algorithm is proven to be sound and complete. Empirical evidence shows practical advantages of our approach for certain categories of graphs.

Original languageEnglish
Pages (from-to)1441-1456
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume18
Issue number11
DOIs
StatePublished - 1 Jan 2006

Keywords

  • Database applications
  • Web mining
  • data mining
  • graph mining
  • mining methods and algorithms

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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