SLIQ: A fast scalable classifier for data mining

  • Manish Mehta
  • Rakesh Agrawal
  • Jorma Rissanen
Data Mining
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1057)


Classification is an important problem in the emerging field of data mining. Although classification has been studied extensively in the past, most of the classification algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large data sets. This paper discusses issues in building a scalable classifier and presents the design of SLIQ, a new classifier. SLIQ is a decision tree classifier that can handle both numeric and categorical attributes. It uses a novel pre-sorting technique in the tree-growth phase. This sorting procedure is integrated with a breadth-first tree growing strategy to enable classification of disk-resident datasets. SLIQ also uses a new tree-pruning algorithm that is inexpensive, and results in compact and accurate trees. The combination of these techniques enables SLIQ to scale for large data sets and classify data sets irrespective of the number of classes, attributes, and examples (records), thus making it an attractive tool for data mining.


Decision Tree Leaf Node Numeric Attribute Categorical Attribute Tree Pruning 
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 1996

Authors and Affiliations

  • Manish Mehta
    • 1
  • Rakesh Agrawal
    • 1
  • Jorma Rissanen
    • 1
  1. 1.IBM Almaden Research CenterSan Jose

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