An algorithm for computing attribute reducts based on graph search strategy

Abstract

Attribute reducts can discover previously unknown, non-trivial and useful abstractions from the data in large databases. However, many methods for finding attribute reducts from large data sets always meet a difficult problem of combination explosion. To overcome the problem and find some attribute reducts with high efficiency, the algorithm CARHS was proposed. The basic idea of CARHS is: 1) transform the problem into an equivalent one that searches paths, from which attribute reducts can be easily derived, from a graph; 2) employ high efficient heuristic rules during the course of depth-first search on the graph. By means of the heuristic rules, those paths that would not derive attribute reducts could be blocked as early as possible, furthermore, for those paths that would derive the same attribute reduct, only one of them could complete the course of search, and the others could be blocked as early as possible. Thus some attribute reducts could be found by CARHS with high efficiency even when dealing with huge data sets. The transformation of the problem, novel concepts, the heuristic search rules, and the algorithm CARHS were illustrated in detail by some examples. At last, The experiment on three classic UCI data sets showed the effect of the heuristic search rules and the efficiency of the algorithm CARHS.