The VLDB Journal

, Volume 15, Issue 3, pp 250–262 | Cite as

Dependency trees in sub-linear time and bounded memory

Regular Paper

Abstract

We focus on the problem of efficient learning of dependency trees. Once grown, they can be used as a special case of a Bayesian network, for PDF approximation, and for many other uses. Given the data, a well-known algorithm can fit an optimal tree in time that is quadratic in the number of attributes and linear in the number of records. We show how to modify it to exploit partial knowledge about edge weights. Experimental results show running time that is near-constant in the number of records, without significant loss in accuracy of the generated trees.

Keywords

Data mining Probably approximately correct learning Fast algorithms Dependency trees 

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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.IBM Haifa LabsHaifaIsrael
  2. 2.Robotics InstituteCarnegie-Mellon UniversityPittsburgh

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