Clustering Algorithms for ITS Sequence Data with Alignment Metrics

  • Andrei Kelarev
  • Byeong Kang
  • Dorothy Steane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


The article describes two new clustering algorithms for DNA nucleotide sequences, summarizes the results of experimental analysis of performance of these algorithms for an ITS-sequence data set, and compares the results with known biologically significant clusters of this data set. It is shown that both algorithms are efficient and can be used in practice.


Cluster Algorithm Internal Transcribe Spacer Alignment Score Average Success Rate Practical Machine Learn Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andrei Kelarev
    • 1
  • Byeong Kang
    • 1
  • Dorothy Steane
    • 2
  1. 1.School of ComputingUniversity of TasmaniaTasmaniaAustralia
  2. 2.CRC Forestry and School of Plant ScienceUniversity of TasmaniaTasmaniaAustralia

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