Efficiently mining frequent trees in a forest

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Efficiently mining frequent trees in a forest

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Edmonton, Alberta, Canada

SESSION: Graphs and trees table of contents

Pages: 71 - 80

Year of Publication: 2002

ISBN:1-58113-567-X

Author

Mohammed J. Zaki

Rensselaer Polytechnic Institute, Troy NY

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
: AAAI

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 37, Downloads (12 Months): 135, Citation Count: 76

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ABSTRACT

Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semistructured 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 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 scaleup properties. We also present an application of tree mining to analyze real web logs for usage patterns.

REFERENCES

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