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Logic mining in neural network: reverse analysis method

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Abstract

Neural networks are becoming very popular with data mining practitioners because they have proven through comparison their predictive power with statistical techniques using real data sets. Based on this idea, we will present a method for inducing logical rules from empirical data—Reverse Analysis. When the values of the connections of a neural network resulting from Hebbian learning for the data are given, we hope to know what logical rules are entrenched in it. This method is tested with some real life data sets. In real life data sets, logical rules are assumed to be in conjunctive normal form (CNF) since Horn clauses are inadequate.

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References

  1. Haykin S (1998) Neural network: a comprehensive foundation. Macmillan, New York

    Google Scholar 

  2. Ehyal K, Michael M (2009) Extracting symbolic knowledge from recurrent neural networks—a fuzzy logic approach. Fuzzy Sets Syst 160(2): 145–161

    Article  Google Scholar 

  3. Mantas CJ, Puche JM, Mantas JM (2006) Extraction of similarity based fuzzy rules from artificial neural networks. Int J Approx Reason 43: 202–221

    Article  MATH  MathSciNet  Google Scholar 

  4. Kasabov N (2006) Adaptation and interaction in dynamical systems; Modelling and rule discovery through evolving connectionist systems. Appl Soft Comput 6: 307–322

    Article  Google Scholar 

  5. d’Avila Garcez AS, Broda K, Gabbay DM (2002) Neural-symbolic learning systems: foundations and applications. In: Perspectives in neural computing. Springer, Berlin. ISBN:1-85233-512-2

  6. Wan Abdullah WAT et al (1991) Neural network logic. In: Benhar O et al (eds) Neural networks: from biology to high energy physics. ETS Editrice, Pisa, pp 135–142

    Google Scholar 

  7. Wan Abdullah WAT (1992) Logic programming on a neural network. Int J Intell Syst 7: 513–519

    Article  Google Scholar 

  8. Wan Abdullah WAT (1993) The logic of neural networks. Phys Lett 176A: 202–206

    Google Scholar 

  9. Wan Abdullah WAT, Sathasivam S (2005) Logic mining using neural networks. In: Proceedings of the international conference in intelligent system 2005 (ICIS 2005), Kuala Lumpur, Malaysia

  10. Abdullah W, Tajuddin WA, Sathasivam S (2006) Logical rules induction. In: Proceedings of the 2nd IMT-GT regional conference on mathematics, statistics and applications, Penang, Malaysia

  11. Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biol Cyber 52: 141–152

    MATH  MathSciNet  Google Scholar 

  12. Sathasivam S (2007) Logic mining in neural network. PhD thesis, Malaysia

  13. Geszti T (1990) Physical models of neural networks. World Scientific, New Jersey

    MATH  Google Scholar 

  14. Sathasivam S, Abdullah W, Tajuddin WA (2007) Flatness of the energy landscape for Horn ClausesLogic. Matematika 23: 147–156

    Google Scholar 

  15. Sathasivam S, Abdullah W, Tajuddin WA (2008) Logic learning in Hopfield networks. Mod Appl Sci 2: 57–63

    Google Scholar 

  16. Liberatore P (2004) Redundancy in Logic I: propositional formula. Artif Intell 163: 135–142

    MathSciNet  Google Scholar 

  17. Raj A (2005) Private communication

  18. Roberto J, Bayardo JR, Agrawal R (1999) Mining the most interesting rules. In: ACM SIGKOD international conference on knowledge discovery and data mining in USA, pp 145–154

  19. Piatetsky-Shapiro G (1991) Discovery, analysis, and presentation of strong rules. Knowledge discovery in databases. AAAI/MIT Press, Cambridge, pp 229–248

    Google Scholar 

  20. Do TD, Hui SC, Fong ACM (2005) Prediction confidence for associative classification. In: Proceedings of the fourth international conference on machine learning and cybernetics, Guangzhou, pp 1993–1998

  21. Verykios VS, Elmagarmid AK, Bertino E, Saygin Y, Dasseni E (2004) Association rule hiding. IEEE Trans Knowl Data Eng 16(4): 434–447

    Article  Google Scholar 

  22. Zaki MJ (2000) Generating non-redundant association rules. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pp 34–43

  23. Lu Y, Lu S, Fotouhi F, Sun Y, Yang Z, Liang LR (2006) PDC: pattern discovery with confidence in DNA sequences. In: Proceedings of advances in computer science and technology, Mexico

  24. Zhang S, Lu J, Zhang C (2004) A fuzzy logic based method to acquire user threshold of minimum support for mining association rules. Int J Inf Sci Inform Comp Sci 164(1–4): 1–16

    MATH  Google Scholar 

  25. Xiong H, Tan PN, Kumar V (1993) Mining strong affinity association patterns in data set with skewed support. In: Proceedings of 3rd IEEE international conference on data mining

Download references

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Authors and Affiliations

  1. School of Science Mathematics, Universiti Sains Malaysia, 11800, Penang, Malaysia

    Saratha Sathasivam

  2. Physics Department, Faculty of Science, University Malaya, 50603, Kuala Lumpur, Malaysia

    Wan Ahmad Tajuddin Wan Abdullah

Authors
  1. Saratha Sathasivam
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  2. Wan Ahmad Tajuddin Wan Abdullah
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Corresponding author

Correspondence to Saratha Sathasivam.

Additional information

Communicated by R. Neruda.

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Sathasivam, S., Wan Abdullah, W.A.T. Logic mining in neural network: reverse analysis method. Computing 91, 119–133 (2011). https://doi.org/10.1007/s00607-010-0117-9

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  • DOI: https://doi.org/10.1007/s00607-010-0117-9

Keywords

Mathematics Subject Classification (2000)

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