Mining Classification Rules from Datasets with Large Number of Many-Valued Attributes

  • Giovanni Giuffrida
  • Wesley W. Chu
  • Dominique M. Hanssens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1777)


Decision tree induction algorithms scale well to large datasets for their univariate and divide-and-conquer approach. However, they may fail in discovering effective knowledge when the input dataset consists of a large number of uncorrelated many-valued attributes. In this paper we present an algorithm, Noah, that tackles this problem by applying a multivariate search. Performing a multivariate search leads to a much larger consumption of computation time and memory, this may be prohibitive for large datasets. We remedy this problem by exploiting effective pruning strategies and efficient data structures. We applied our algorithm to a real marketing application of cross-selling. Experimental results revealed that the application database was too complex for C4.5 as it failed to discover any useful knowledge. The application database was also too large for various well known rule discovery algorithms which were not able to complete their task. The pruning techniques used in Noah are general in nature and can be used in other mining systems.


Association Rule Minimum Support Term Dependency Input Dataset Default Rule 
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 2000

Authors and Affiliations

  • Giovanni Giuffrida
    • 1
  • Wesley W. Chu
    • 1
  • Dominique M. Hanssens
    • 2
  1. 1.Dept. of Computer ScienceUniv. of CaliforniaLos Angeles
  2. 2.Anderson Grad. School of Manag.Univ. of CaliforniaLos Angeles

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