Skip to main content
SpringerLink
Log in
Book cover

Computational Intelligence in Information Systems pp 109–118Cite as

Modeling Associative Memories Using Formal Concept Analysis

  • Conference paper

  • 1288 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 331))

Abstract

Associative memory is one of the primary functions of the human brain. In the literature, there are several neural networks based models that represent associative memory with the help of pattern associations. In this paper, we model the associative memory activity using Formal Concept Analysis (FCA), which is a standard technique for data and knowledge processing. In our proposal, patterns are associated with the help of object-attribute relations and the memory is represented using the formal concepts generated using FCA. We show that the extent and intent relations in the concepts help us to recall the patterns bi-directionally. Further, we model the pattern recall process for the given input even when the exact match is not found in the memory, using the concept hierarchies in the concept lattice.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Sivanandam, S.N., Deepa, S.N.: Principles of Soft Computing, 2nd edn. Wiley India (2011)

    Google Scholar 

  2. Ganter, B., Wille, R., Franzke, C.: Formal Concept Analysis: Mathematical Foundation. Springer (1999)

    Google Scholar 

  3. Zarate, L.E., Mariano Dias, S., Junho Song, M.A.: FCANN: A new approach for extracting and representing knowledge from ANN trained via Formal Concept Analysis. Neurocomputing 71, 2670–2684 (2008)

    Article  Google Scholar 

  4. Zarate, L.E., Mariano Dias, S.: Qualitative behavior rules for the cold rolling process extracted from trained ANN via the FCANN method. Engineering Applications of Artificial Intelligence 22, 718–731 (2009)

    Article  Google Scholar 

  5. Endres, D., Foldiak, P., Priss, U.: An application of Formal Concept Analysis to Semantic Neural Decoding. Annals of Mathematics and Artificial Intelligence 57, 233–248 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  6. Aravind Kumar, M.: Information Retrieval using Concept Lattices. Technical Report, University of Cincinnati (2006)

    Google Scholar 

  7. Carpineto, C., Romano, G.: Conceptual Data Analysis. John Wiley and Sons Ltd. (2004)

    Google Scholar 

  8. Aswani Kumar, Ch., Annapurna, J.: Exploring Attributes with Domain Knowledge in Formal Concept Analysis. Journal of Computing and Information Technology 21, 109–123 (2013)

    Google Scholar 

  9. Aswani Kumar, Ch., Srinivas, S.: Concept Lattice using Fuzzy K-means Clustering. Expert Systems with Applications 37(3), 2696–2704 (2010)

    Article  Google Scholar 

  10. Aswani Kumar, Ch.: Mining association rules using non-negative matrix factorization and formal concept analysis. In: Venugopal, K.R., Patnaik, L.M. (eds.) ICIP 2011. CCIS, vol. 157, pp. 31–39. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Poelmans, J., Ignatov, D.I., Kuzentsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert System with Applications 40, 6538–6560 (2013)

    Article  Google Scholar 

  12. Gärdenfors, P.: Conceptual Space as a framework of Knowledge Representation. Mind and Matter 2(2), 9–27 (2004)

    Google Scholar 

  13. Raubal, M.: Benjamin Adams, The semantic Web needs More Cognition. Semantic Web 1, 69–74 (2010)

    Google Scholar 

  14. Kitto, K., Bruza, P., Gabora, L.: A Quantum Information Retrieval Approach to Memory. In: Proceedings of International Joint Conference on Neural Networks (2012)

    Google Scholar 

  15. Wang, Y., Wang, Y., Patel, S., Patel, D.: A Layered Reference Model of the Brain. IEEE Transaction on System, Man and Cybernetics 36(2) (2006)

    Google Scholar 

  16. Belohlavek, R., Trnecka, M.: Basic Level in Formal Concept Analysis: Interesting Concepts and Psychological Ramifications. In: Proceedings of Twenty-Third International Joint Conference on Artificial Intelligence, pp. 1233–1239 (2013)

    Google Scholar 

  17. Goguen, J.: Mathematical models of cognitive space and time. In: Andler, D., Ogawa, Y., Okada, M., Watanabe, S. (eds.) Reasoning and Cognition: Proc. of the Interdisciplinary Conference on Reasoning and Cognition, pp. 125–128. Keio University Press (2006)

    Google Scholar 

  18. Dyce, W., Marmin, T., Patel, N., Sipieter, C., Tisserant, G., Prince, V.: Let the System Learn a Game: How FCA can Optimize a Cognitive Memory Structure. In: 20th European Conference on Artificial Intelligence (2012)

    Google Scholar 

  19. Zarate, L.E., Song, M., Alvarez, A., Soares, B., Nogueira, B., Vimieiro, R., Dias, S., Santos, T., Vieria, N.: An approach to knowledge extraction from ANN through Formal Concept Analysis-Computational Tool proposal: SOPHIANN, vol. 1, pp. 43–48, 9–13 (2006)

    Google Scholar 

  20. Xu, W., Pang, J., Luo, S.: A novel cognitive system model and approach to transformation of information granules. International Journal of Approximate Reasoning 55, 853–866 (2014)

    Article  MathSciNet  Google Scholar 

  21. Rudolph, S.: Using FCA for Encoding Closure Operators into Neural Networks. In: Priss, U., Polovina, S., Hill, R. (eds.) ICCS 2007. LNCS (LNAI), vol. 4604, pp. 321–332. Springer, Heidelberg (2007)

    Google Scholar 

  22. Chella, A., Pirrone, R., Sorbello, R., Jóhannsdóttir, K.R. (eds.): Biologically Inspired Cognitive Architectures 2012. AISC, vol. 196, pp. 33–34. Springer, Heidelberg (2013)

    Book  Google Scholar 

  23. Belohlavek, R.: Fuzzy Logical Bidirectional Associative Memory. Information Science 128, 91–103 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  24. Belohlavek, R.: Representation of Concept Lattice by Bidirectional Associative Memories. Neural Computation 12, 2279–2290 (2000)

    Article  Google Scholar 

  25. Acevedo, M.E., Yanez-Marquez, C., Acevedo, M.A.: Associative Models for Storing and Retrieving Concept Lattice. Mathematical Problems in Engineering, Article ID 356029 (2010)

    Google Scholar 

  26. Aswani Kumar, Ch., Singh, P.K.: Knowledge Representation Using Formal Concept Analysis: A Study on Concept Generation. In: Global Trends in Knowledge Representation and Computational Intelligence, pp. 306–336. IGI Global Publishers (2014)

    Google Scholar 

  27. Dataset from Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets

  28. Data set Normalization: 1) Cholesterol: www.heart.org ; www.vaughns-1-pagers.com 2) Glucose: en.wikipedia.org/wiki/Blood_sugar ; abcnews.go.com/health 3) HDL: en.wikipedia.org/wiki/High-density_lipoprotein ; www.heart.org 4) Ratio: www.mayoclinic.org ; www.exrx.net 5) Glycolated Hemoglobin: www.webmd.com ; en.wikipedia.org/wiki/Glycated_hemoglobin 6) BMI: www.en.wikipedia.org/wiki/Body_Mass_Index 7) Hip/Waist Ratio: www.virginactive.co.uk

  29. http://www.liacs.nl/~edegraaf/assign1.html

  30. Basheer, I.A., Hajmeer, M.: Artificial Neural Networks: fundamentals, computing, design and application. Journal of Microbiological Methods 43, 3–31 (2000)

    Article  Google Scholar 

  31. Benitez, J.M., Castro, J.L., Requena, I.: Are Atrificial Neural Network Black Boxes? IEEE Transaction on Neural Netwrok 8, 1156–1164 (1997)

    Article  Google Scholar 

  32. Kriegel, F.: Incremental computation of concept lattice. Studia-Univ. Babes, Bolyai, Informatica, vol. LIX(1) (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology & Engineering, VIT University, Vellore, India

    Cherukuri Aswani Kumar & M. S. Ishwarya

  2. Dept. Of Artificial Intelligence, Faculty of CS and IT, University of Malaya, Kuala Lumpur, Malaysia

    Chu Kiong Loo

Authors
  1. Cherukuri Aswani Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. S. Ishwarya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chu Kiong Loo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cherukuri Aswani Kumar .

Editor information

Editors and Affiliations

  1. Jalan Tungku Link, Institut Teknologi Brunei, Gadong, Brunei Darussalam

    Somnuk Phon-Amnuaisuk

  2. Jalan Tungku Link, Institut Teknologi Brunei, Gadong, Brunei Darussalam

    Thien Wan Au

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Aswani Kumar, C., Ishwarya, M.S., Kiong Loo, C. (2015). Modeling Associative Memories Using Formal Concept Analysis. In: Phon-Amnuaisuk, S., Au, T. (eds) Computational Intelligence in Information Systems. Advances in Intelligent Systems and Computing, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-319-13153-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13153-5_11

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13152-8

  • Online ISBN: 978-3-319-13153-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation