Computationally efficient mining for fuzzy implication-based association rules in quantitative databases
Association rule mining is one of the focal points in research on knowledge discovery. While conventional approaches usually deal with databases with binary values, this paper presents an approach to discovering association rules (such as X double right arrow Y) from quantitative datasets, which are commonly seen in real-world applications. Primary attention is paid to association rules with degrees of support and implication ( ARsi), taking a more logic-oriented viewpoint for X-to-Y relationships. Fuzzy logic is applied to "discretization" of quantitative domains as well as to logic implication operations so as to remedy possible boundary problems due to sharp partitioning and facilitate fuzzy implication, respectively. In doing so, a mining algorithm ( FIAR) is proposed to discover ARsi, in that several properties of t-norms and fuzzy implication operators (FIOs) are investigated to reduce times of scanning databases. Furthermore, simple rules are discussed and incorporated as optimization strategies into the algorithm. Finally, experiments with synthetic data as well as with real datasets are carried out to show the performance of the proposed algorithm.
fuzzy implication association rules simple rules data mining