Knowledge and Information Systems

, Volume 41, Issue 3, pp 559–590 | Cite as

EvoMiner: frequent subtree mining in phylogenetic databases

  • Akshay Deepak
  • David Fernández-Baca
  • Srikanta Tirthapura
  • Michael J. Sanderson
  • Michelle M. McMahon
Regular Paper


The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the well-known maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like levelwise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest-common-ancestor-based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speedups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth-first enumeration mode to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority-rule trees—two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set.


Data mining Pattern discovery Maximum agreement subtree  Phylogenetics Evolutionary bioinformatics 



This work was supported in part by National Science Foundation Grant DEB-0829674. The authors thank Drs. Sen Zhang and Jason T. L. Wang for sharing the source code of Phylominer and discussions on their work. They also thank Drs. Seung-Jin Sul and Tiffani L. Williams for sharing the datasets from Bayesian analyses, and Dr. Nicholas D. Pattengale for sharing the datasets consisting of bootstrapped trees. A special thanks to the anonymous reviewers at KAIS whose detailed comments helped greatly in improving the paper.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Akshay Deepak
    • 1
  • David Fernández-Baca
    • 1
  • Srikanta Tirthapura
    • 2
  • Michael J. Sanderson
    • 3
  • Michelle M. McMahon
    • 4
  1. 1.Department of Computer ScienceIowa State UniversityAmesUSA
  2. 2.Department of Electrical and Computer EngineeringIowa State UniversityAmesUSA
  3. 3.Department of Ecology and Evolutionary BiologyUniversity of ArizonaTucsonUSA
  4. 4.Department of Plant SciencesUniversity of ArizonaTucsonUSA

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