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

  • Published:2018-10 Issue: Volume:158 Page:75-80
  • ISSN:0950-7051
  • Container-title:Knowledge-Based Systems
  • language:en
  • Short-container-title:Knowledge-Based Systems

Author:

Trnecka MartinORCID,Trneckova Marketa

Funder

Czech Science Foundation

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference20 articles.

1. The Set Basis Problem is NP-complete;Stockmeyer,1975

2. From-below approximations in Boolean matrix factorization: geometry and new algorithm;Belohlavek;JCSS,2015

3. Formal Concept Analysis Mathematical Foundations;Ganter,1999

4. What is the dimension of your binary data?;Tatti,2006

5. How to assess quality of BMF algorithms?;Belohlavek,2016

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1. Attribute granules-based object entropy for outlier detection in nominal data;Engineering Applications of Artificial Intelligence;2024-07

2. Revisiting data reduction for boolean matrix factorization algorithms based on formal concept analysis;International Journal of Machine Learning and Cybernetics;2024-06-11

3. Factorizing formal contexts from closures of necessity operators;Computational and Applied Mathematics;2024-03-23

4. What is the Intrinsic Dimension of Your Binary Data?—and How to Compute it Quickly;Lecture Notes in Computer Science;2024

5. Skill hierarchy structure based skill assessment and learning paths;International Journal of General Systems;2023-09-14
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