Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.knosys.2018.05.035 | Knowledge-Based Systems |
Keywords | Field | DocType |
Boolean matrix factorization,Formal concept analysis,Data reduction | Row,Computer science,Boolean matrix factorization,Algorithm,Size reduction,Formal concept analysis,Data reduction | Journal |
Volume | ISSN | Citations |
158 | 0950-7051 | 0 |
PageRank | References | Authors |
0.34 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Trnecka | 1 | 3 | 2.10 |
| Marketa Trneckova | 2 | 3 | 3.46 |