Knowledge discovery from numerical data

摘要

One of the authors previously presented an algorithm for discovering understandable propositions from numerical data. The algorithm consists of normalization, multiple regression analysis and the approximation of multilinear functions by continuous Boolean functions. Continuous Boolean functions are included in the space of multilinear functions. The space of multilinear functions can be corresponded to the space of probability distributions, using the principle of indifference. The distance between two probability distributions is described by Kullback-Leibler (KL) information. Thus, the distance between multilinear functions and continuous Boolean functions is described using KL information. However, since the approximation algorithm is exponential in computational complexity, this paper presents a polynomial approximation algorithm.