A method for mining quantitative association rules

  1. María N. Moreno 1
  2. Saddys Segreda 1
  3. Vivian F. López 1
  4. M. José Polo 1
  1. 1 Department of Computing and Automatic, University of Salamanca, Salamanca, Spain
Aktak:
SMO'06: Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization

Argitaletxea: World Scientific and Engineering Academy and Society (WSEAS)

ISBN: 9789608457539,  960845753X

Argitalpen urtea: 2006

Orrialdeak: 173-178

Biltzarra: SMO'06: Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, September 2006

Mota: Biltzar ekarpena

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Laburpena

Association rule mining is a significant research topic in the knowledge discovery area. In the last years a great number of algorithms have been proposed with the objective of solving diverse drawbacks presented in the generation of association rules. One of the main problems is to obtain interesting rules from continuous numeric attributes. In this paper, a method for mining quantitative association rules is proposed. It deals with the problem of discretizing continuous data in order to discover a manageable number of high confident association rules, which cover a high percentage of examples in the data set. The method was validated by applying it to data from software project management metrics.