IMB3-Miner: Mining Induced/Embedded Subtrees by Constraining the Level of Embedding

  • Henry Tan
  • Tharam S. Dillon
  • Fedja Hadzic
  • Elizabeth Chang
  • Ling Feng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


Tree mining has recently attracted a lot of interest in areas such as Bioinformatics, XML mining, Web mining, etc. We are mainly concerned with mining frequent induced and embedded subtrees. While more interesting patterns can be obtained when mining embedded subtrees, unfortunately mining such embedding relationships can be very costly. In this paper, we propose an efficient approach to tackle the complexity of mining embedded subtrees by utilizing a novel Embedding List representation, Tree Model Guided enumeration, and introducing the Level of Embedding constraint. Thus, when it is too costly to mine all frequent embedded subtrees, one can decrease the level of embedding constraint gradually up to 1, from which all the obtained frequent subtrees are induced subtrees. Our experiments with both synthetic and real datasets against two known algorithms for mining induced and embedded subtrees, FREQT and TreeMiner, demonstrate the effectiveness and the efficiency of the technique.


Minimum Support Association Rule Mining Depth First Search Extension Point Tree Structure Data 
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

  • Henry Tan
    • 1
  • Tharam S. Dillon
    • 1
  • Fedja Hadzic
    • 1
  • Elizabeth Chang
    • 2
  • Ling Feng
    • 3
  1. 1.Faculty of Information TechnologyUniversity of Technology SydneySydneyAustralia
  2. 2.School of Information SystemCurtin University of TechnologyPerthAustralia
  3. 3.Department of Computer ScienceUniversity of TwenteEnschedeNetherlands

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