Efficient Algorithms for Association Finding and Frequent Association Pattern Mining

  • Gong Cheng
  • Daxin Liu
  • Yuzhong Qu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9981)


Finding associations between entities is a common information need in many areas. It has been facilitated by the increasing amount of graph-structured data on the Web describing relations between entities. In this paper, we define an association connecting multiple entities in a graph as a minimal connected subgraph containing all of them. We propose an efficient graph search algorithm for finding associations, which prunes the search space by exploiting distances between entities computed based on a distance oracle. Having found a possibly large group of associations, we propose to mine frequent association patterns as a conceptual abstract summarizing notable subgroups to be explored, and present an efficient mining algorithm based on canonical codes and partitions. Extensive experiments on large, real RDF datasets demonstrate the efficiency of the proposed algorithms.


Association finding Canonical code Distance oracle Frequent association pattern mining Graph search 



This work was supported by the 863 Program under Grant 2015AA015406, the NSFC under Grant 61572247 and 61223003, and the Fundamental Research Funds for the Central Universities.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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