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

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

ISBN: 9789608457539,  960845753X

Año de publicación: 2006

Páginas: 173-178

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

Tipo: Aportación congreso

GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

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.