Mining concepts from incomplete datasets utilizing matrix factorization

CEUR Workshop Proceedings

Volumn 972, Issue , 2012, Pages 33-44

  Pisková, Lenka ^a   Pero, Štefan ^a   Horváth, Tomáš ^b   Krajči, Stanislav ^a  

^a P J AFÁRIK UNIVERSITY   (Slovakia)

^b UNIVERSITY OF HILDESHEIM   (Germany)

Author keywords

Clustering; Formal Concept Analysis; Matrix factorization; Missing values completion

Indexed keywords

ARTIFICIAL INTELLIGENCE; FACTORIZATION; INFORMATION ANALYSIS; LEARNING SYSTEMS; MATRIX ALGEBRA; STOCHASTIC SYSTEMS;

CLUSTERING; CONCEPT ANALYSIS; DENSE MATRICES; MATRIX FACTORIZATIONS; MISSING VALUES; REAL DATA SETS; SIMPLE STOCHASTIC; UCI MACHINE LEARNING REPOSITORY;

FORMAL CONCEPT ANALYSIS;

EID: 84913582978     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. B. Ganter, R. Wille: Formal concept analysis: Mathematical foundations. Springer, 1999, ISBN 3-540-62771-5.
2. P. Burmeister, R. Holzer: Treating Incomplete Knowledge in Formal Concept Analysis. In: B. Ganter, G. Stumme, R. Wille (Eds.): Formal Concept Analysis: State of the Art, LNAI 3626, Springer, Heidelberg, 2005.
3. L. Ben Othman, S. Ben Yahia: Yet Another Approach for Completing Missing Values. CLA 2006, pages 155-169.
4. MV C: Generic Basis of Association Rules Based Approach for Missing Values Completion. The International Journal of Computing and Information Sciences (IJCIS), 2008.
5. S. Ben Yahia, Kh. Arour, A. Jaoua: Completing Missing Values Using Discovered Formal Concepts. DEXA 2000, pages 741-751.
6. S. Ben Yahia, Kh. Arour.: Mining fuzzy formal concepts for completing missing values. In Proceedings of the Intl. Symposium on Innovation in Information and Communication Technology, Amman, Jordan, 2002.
7. M. Krupka, J. Lastovicka: Concept lattices of incomplete data. In: F. Domenach, D.I. Ignatov, and J. Poelmans (Eds.): ICFCA 2012, LNAI 7278. Springer, Heidelberg, pages 180-194, 2012.
8. J. Liu, X. Yao: Formal Concept Analysis of Incomplete Information System. In: FSKDIEEE (2010), pages 2016-2020.
9. P. Gajdoš, P. Moravec, V. Snášel: Concept Lattice Generation by Singular Value Decomposition, In: V. Snášel, R. Bělohlávek (Eds.): CLA 2004, pages 102-110.
10. V. Snášel, P. Gajdoš, H. Abdulla, M. Polovinčák: Using Matrix Decompositions in Formal Concept Analysis. ISIM 2007, pages 121-128.
11. V. Snášel, H. Abdulla, M. Polovinčák: Using Nonnegative Matrix Factorization and Concept Lattice Reduction to visualizing data. SYRCoDIS, 2007.
12. R. Bělohlávek, V. Vychodil: Factor Analysis of incidence data via Novel Decomposition of Matrices, In: S. Ferré and S. Rudolph (Eds.): ICFCA 2009, LNAI 5548, pages 83-97, 2009.
13. R. Bělohlávek, V. Vychodil: Discovery of Optimal Factors in binary data via novel method of matrix decomposition. Journal of Computer and System Sciences 76(1)(2010), pages 3-20.
14. Y. Koren, R. Bell and C. Volinsky. Matrix Factorization Techniques for Recommender Systems. COMPUTER (Vol. 42, 8): IEEE Computer Society Press, Los Alamitos, USA, 2009.
15. G. Takács, I. Pilászy, B. Németh. Investigation of Various Matrix Factorization Methods for Large Recommender Systems. KDD Netflix workshop, 2008.
16. L. Bottou. Stochastic learning. In Bousquet, O. and von Luxburg, U., editors, Advanced Lectures on Machine Learning, Lecture Notes in Artificial Intelligence, LNAI 3176, pages 146-168. Springer Verlag, Berlin.
17. Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, L. Schmidt-Thieme. Learning attribute-to-feature mappings for cold-start recommendations. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010). IEEE Computer Society, 2010.
18. T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition, Springer Series in Statistics, 2009.
19. J. Bergstra, Y. Bengio: Random Search for Hyper-Parameter Optimization. J. Machine Learning Research 13, pages 281-305, 2012.