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Data Reduction for Boolean Matrix Factorization Algorithms Based on Formal Concept Analysis
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DOI：10.1016/j.knosys.2018.05.035.png)
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Abstract
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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.
Keywords:
Boolean matrix factorization
Formal concept analysis
Data reduction
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