Efficient reduction of the number of associations rules using fuzzy clustering on the data

Abstract

In this paper, we are interested in the knowledge discovery methods. The major inconveniences of these methods are: i) the generation of a big number of association rules that are not easily assimilated by the human brain ii) the space memory and the time execution necessary for the management of their data structures. To cure this problem, we propose to build rules (meta-rules) between groups (or clusters) resulting from a preliminary fuzzy clustering on the data. We prove that we can easily deduce knowledge about the initial data set if we want more details. This solution reduced considerably the number of generated rules, offered a better interpretation of the data and optimized both the space memory and the execution time. This approach is extensible; the user is able to choose the fuzzy clustering or the extraction rules algorithm according to the domain of his data and his needs. © 2011 Springer-Verlag.