Data reduction for Boolean matrix factorization algorithms based on formal concept analysis

Abstract

Data size reduction is an important step in many data mining techniques. We present a novel approach based on formal concept analysis to data reduction tailored for Boolean matrix factorization methods. A general aim of these methods is to find factors that exactly or approximately explain data. The presented approach is able to significantly reduce the size of data by choosing a representative set of rows, and preserve (with a little loss) factors behind the data, i.e. it only slightly affects a quality of the factors produced by Boolean matrix factorization algorithms.