Finding minimal reduct with binary integer programming in data mining

The search for the minimum size of reduct is based on the assumption that, within the data set, there are some attributes that are more important than the rest. In this paper, we present an algorithm for finding minimum-size reducts which is based on a rough set approach and a dedicated decision-related binary integer programming (BIP) algorithm. The algorithm transforms an equivalence class obtained from a decision system into a BIP model. An algorithm for solving the BIP is given. The presented work has links to rough set theory, data mining and nonmonotonic reasoning.