Efficient Data-Reduction Methods for On-Line Association Rule Discovery

  title={Efficient Data-Reduction Methods for On-Line Association Rule Discovery},
  author={Herve Bronnimann and Bin Chen and Manoranjan Dash and Peter Haas and Yi Qiao and Peter Scheuermann},
Classical data mining algorithms that require one or more computationally intensive passes over the entire database can be prohibitively slow. One effective method for dealing with this ever-worsening scalability problem is to run the algorithms on a small sample of the data. We present and empirically compare two data-reduction algorithms for producing such a sample; these algorithms, called FAST and EA, are tailored to “count” data applications such as association-rule mining. The algorithms… CONTINUE READING
Highly Cited
This paper has 37 citations. REVIEW CITATIONS

Similar Papers

Loading similar papers…