A hybrid model for rule discovery in data

摘要

This paper presents a hybrid model for rule discovery in real world data with uncertainly and incompleteness. The hybrid model is created by introducing an appropriate relationship between deductive reasoning and stochastic process, and extending the relationship so as to include abduction. Furthermore, a generalization distribution table (GDT), which is a variant of transitions matrix in stochastic process, is defined. Thus, the typical methods of symbolic reasoning such as deduction, induction, and abduction, as well as the methods based on soft computing techniques such as rough sets, fuzzy sets, and granular computing can be cooperatively used by taking the GDT and/or the transition matrix in stochastic process as mediums. Ways of implementation of the hybrid model are also discussed.