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TreeMiner: An Efficient Algorithm for Mining Embedded Ordered Frequent Trees

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Advanced Methods for Knowledge Discovery from Complex Data

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Summary

Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semi-structured data, and so on. We formulate the problem of mining (embedded) subtrees in a forest of rooted, labeled, and ordered trees. We present TreeMiner, a novel algorithm to discover all frequent subtrees in a forest, using a new data structure called a scope-list. We contrast TreeMiner with a pattern-matching tree-mining algorithm (PatternMatcher). We conduct detailed experiments to test the performance and scalability of these methods. We find that TreeMiner outperforms the pattern matching approach by a factor of 4 to 20, and has good scale-up properties. We also present an application of tree mining to analyze real web logs for usage patterns.

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Authors
  1. Mohammed J. Zaki
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© 2005 Dr Sanghamitra Bandyopadhyay

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Zaki, M.J. (2005). TreeMiner: An Efficient Algorithm for Mining Embedded Ordered Frequent Trees. In: Advanced Methods for Knowledge Discovery from Complex Data. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-284-5_5

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  • DOI: https://doi.org/10.1007/1-84628-284-5_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-989-0

  • Online ISBN: 978-1-84628-284-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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