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2011 | 21 | 4 | 745-756
Tytuł artykułu

Knowledge discovery in data using formal concept analysis and random projections

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.
Rocznik
Tom
21
Numer
4
Strony
745-756
Opis fizyczny
Daty
wydano
2011
otrzymano
2010-07-18
poprawiono
2010-12-25
poprawiono
2011-02-24
Twórcy
  • Networks and Information Security Division, School of Information Technology and Engineering, VIT University, Vellore, India
Bibliografia
  • Achilioptas, D. (2003). Database friendly random projections: Johnson-Lindenstrauss with binary coins, Journal of Computer and System Sciences 66(4): 671-687.
  • Aswani Kumar, Ch. (2009). Analysis of unsupervised dimensionality reductions, Computer Science and Information Systems 6(2): 217-227.
  • Aswani Kumar, Ch. (2010). Random projections for concept lattice reduction, Proceedings of the 4th International Conference on Information Processing, Bengaluru, India, pp. 1-11.
  • Aswani Kumar, Ch. (2011). Reducing data dimensionality using random projections and fuzzy k-means clustering, International Journal of Intelligent Computing and Cybernetics 4(3): 353-365.
  • Aswani Kumar, Ch. and Srinivas, S. (2006). Latent semantic indexing using eigenvalue analysis for efficient information retrieval, International Journal of Applied Mathematics and Computer Science 16(4): 551-558.
  • Aswani Kumar, Ch. and Srinivas, S. (2010a). Concept lattice reduction using fuzzy k-means clustering, Expert Systems with Applications 9(1): 2696-2704.
  • Aswani Kumar, Ch. and Srinivas, S. (2010b). Mining associations in health care data using formal concept analysis and singular value decomposition, Biological Systems 18(4): 787-807.
  • Aswani Kumar, Ch. and Srinivas, S. (2010c). A note on weighted fuzzy k-means clustering for concept decomposition, Cybernetics and Systems 41(6): 455-467.
  • Belohlavek, R. and Vychodil, V. (2009). Formal concept analysis with background knowledge: Attribute priorities, IEEE Transactions on Systems, Man and Cybernetics 39(4): 399-409.
  • Belohlavek, R. and Vychodil, V. (2010). Discovery of optimal factors in binary data via a novel method of matrix decomposition, Journal of Computer and System Sciences 76(1): 3-20.
  • Bingham, E. and Mannila, H. (2001). Random projections in dimensionality reduction, Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, San Franscisco, CA, USA, pp. 245-250.
  • Carpineto, C. and Romano, G. (2004). Concept Data Analysis: Theory and Applications, John Wiley, Chichester.
  • Divya, R., Aswani Kumar, Ch., Saijanani, S., and Priyadharshini, M. (2011). Deceiving communication links on an organization email corpus, Malaysian Journal of Computer Science 24(1): 17-33.
  • Elloumi, S., Jaam, J., Hasnah, A., Jaoua, A., and Nafkha, I. (2004). A multi-level conceptual data reduction approach based on the Lukasiewicz implication, Information Sciences 163(4): 253-262.
  • Ganter, B. and Wille, R. (1999). Formal Concept Analysis: Mathematical Foundations, Springer, Berlin.
  • Ghosh, P., Kundu, K. and Sarkar, D. (2010). Fuzzy graph representation of fuzzy concept lattice, Fuzzy Sets and Systems 161(12): 1669-1675.
  • Horner, V. (2007). Developing a consumer health informatics decision support system using formal concept analysis, Masters' thesis, University of Pretoria, Pretoria.
  • Jamil, S. and Deogun, J.S. (2001). Concept approximations based on rough sets and similarity measures, International Journal of Applied Mathematics and Computer Science 11(3): 655-674.
  • Kazienko, P. (2009). Mining indirect association rules for web recommendation, International Journal of Applied Mathematics and Computer Science 19(1):165-186, DOI: 10.2478/v10006-009-0015-5.
  • Liu, M., Shao, M., Zhang, W., and Wu, W.C. (2007). Reduction method for concept lattices based on rough set theory and its application, Computers and Mathematics with Applications 53(9): 1390-1410.
  • Pattison, P.E. and Breiger, R.L. (2002). Lattices and dimensional representations: Matrix decompositions and ordering structures, Social Networks 24(4): 423-444.
  • Poelmans, J., Elzinga, P., Viaene, S., Dedene., G. (2010). Formal concept analysis in knowledge discovery: A Survey in R. Goebel (Ed.) Proceedings of the 18th International Conference on Conceptual Structures, Springer-Verlag, Berlin, pp. 139-153.
  • Priss, U. (2006). Formal concept analysis in information science, Annual Review of Information Science and Technology 40(1): 521-543.
  • Snasel, V., Polovincak, M., Dahwa, H.M. and Horak, Z. (2008). On concept lattices and implication bases from reduced contexts, Proceedings of the ICCS Supplement, Toulouse, France, pp. 83-90.
  • Stumme, G. (2009). Formal concept analysis, in S. Staab and R. Studer (Eds.), Handbook on Ontologies, Springer-Verlag, Berlin, pp. 177-199.
  • Stumme, G., Wille, R. and Wille, U. (1998). Conceptual knowledge discovery in databases using formal concept analysis methods, Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery, Nantes, France, pp. 450-458.
  • Valtchev P, Missaoul R, and Godin R. (2004). Formal concept analysis for knowledge discovery and data mining: The new challenges, Proceedings of the 2nd International Conference on Formal Concept Analysis, Sydney, Australia, pp. 352-371.
  • Varmuza, K., Filzmoser, P. and Liebmann, B. (2010). Random projection experiments with chemometric data, Journal of Chemometrics 24(3-4): 209-217.
  • Venter, F.J., Oosthuizen, G.D. and Ross, J.D. (1997). Knowledge discovery in databases using concept lattices, Experts Systems with Applications 13(4): 259-264.
  • Wille, R. (2002). Why can concept lattices support knowledge discovery in databases?, Journal of Experimental and Theoretical Artificial Intelligence 14(2-3): 81-92.
  • Wille, R. (2008). Formal concept analysis as an applied lattice theory, Proceedings of the 4th International Conference on Concept Lattices and Applications, Tunis, Tunisia, pp. 4267.
  • Wu, W.Z., Leung, Y., and Mi, J.S. (2009). Granular computing and knowledge reduction in formal contexts, IEEE Transactions on Knowledge and Data Engineering 21(10): 1461-1474.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv21i4p745bwm
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