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2013 IEEE 13th International Conference on Data Mining (2005)
Houston, Texas
Nov. 27, 2005 to Nov. 30, 2005
ISSN: 1550-4786
ISBN: 0-7695-2278-5
pp: 785-788
Hiroshi Motoda , Osaka University
Marie-Christine Rousset , CNRS, Université Paris-Sud and INRIA
Michèle Sebag , CNRS, Université Paris-Sud and INRIA
Alexandre Termier , Osaka University
Takashi Washio , Osaka University
Kouzou Ohara , Osaka University
ABSTRACT
In this paper, we present a new tree mining algorithm, DRYADEPARENT, based on the hooking principle first introduced in DRYADE [9]. In the experiments, we demonstrate that the branching factor and depth of the frequent patterns to find are key factor of complexity for tree mining algorithms. We show that DRYADEPARENT outperforms the current fastest algorithm, CMTreeMiner, by orders of magnitude on datasets where the frequent patterns have a high branching factor.
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CITATION
Hiroshi Motoda, Marie-Christine Rousset, Michèle Sebag, Alexandre Termier, Takashi Washio, Kouzou Ohara, "Efficient Mining of High Branching Factor Attribute Trees", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 785-788, 2005, doi:10.1109/ICDM.2005.55
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