Mining possibilistic set-valued rules by generating prime disjunctions

摘要

We describe the problem of mining possibilistic set-valued rules in large relational tables containing categorical attributes taking a finite number of values.An example of such a rule might be "IF HOUSEHOLDSIZE={Two OR Tree} AND OCCUPATION={Professional OR Clerical} THEN PAYMENT_METHOD={cashCheck(max=249)OR DebitCart(Max=175)}.the table semantics is supposed to be represented by a frequency distribution,which is interpreted with the help of minimum and maximum operations as a possibility distribution over the corresponding finite multidimensional space.This distribution is approximated by a number of possibilistic prime disjunctions,which represent the strongest patterns.We present an original formal framework generalising the ocnventional boolean approach on the case of (i) finite-valued variables and (ii) continuos-valued semantics,and propose a new algorithm,called Optimist,for the computationally difficult dual transformation which generates all the strongest prime disjunctions (possibilistic patterns) given a table of data.The algorithm consists of generation,absorption and filtration parts.the generated prime disjunctions can then be sued to build rules or for prediction purposes.