Abstract
This chapter summaries the contributions of this book in terms of theoretical significance, practical importance, methodological impact and philosophical aspects. This chapter also identifies and highlights further directions of this research area towards improvement of the research methodologies presented in this book.
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References
Biggs, N., Lloyd, E., Wilson, R.: Graph Theory. Oxford University Press, Oxford (1986)
Bondy, J.A., Murty, U.S.R.: Graph Theory. Springer, Berlin (2008)
Aho, A.V., Hopcroft, J.E., Ullman, J.D.: Data Structures and Algorithms. Addison-Wesley, Boston (1983)
Johnsonbaugh, R.: Discrete Mathematics. Prentice Hall, USA (2008)
Battram, A.: Navigating Complexity: The Essential Guide to Complexity Theory in Business and Management. Spiro Press, London (2002)
Schlager, J.: Systems engineering: key to modern development. IRE Trans. EM. 3(3), 64–66 (1956)
Sage, A.P.: Systems Engineering, San Francisco. Wiley IEEE, CA (1992)
Hall, A.D.: A Methodology for Systems Engineering. Van Nostrand Reinhold, New York (1962)
Goode, H.H., Machol, R.E.: Systems Engineering: An Introduction to the Design of Large-scale Systems. McGraw-Hill, New York (1957)
Aksoy, M.S.: A review of rules family of algorithms. Math. Compu. Appl. 1(13), 51–60 (2008)
Liu, W.Z., White, A. P.: A review of inductive learning. In: Research and Development in Expert Systems VIII, Cambridge (1991)
Mrozek, A.: A new method for discovering rules from examples in expert systems. Int. J. Man Mach. Stud. 36, 127–143 (1992)
Hart, A.: Knowledge Acquisition for Expert systems. Chapman and Hall, London (1989)
Quinlan, R.: Induction, knowledge and expert systems. In: Artificial Intelligence Developments and Applications, Amsterdam (1988)
Quinlan, R.: Inductive knowledge acquisition: a case study. In: Quinlan, R. (ed.) Applications of Expert Systems, pp. 157–173. Turing Institute Press, UK (1987)
Michalski, R., Mozetic, I., Hong, J., Lavrac, N.: The multi-purpose incremental learning system AQ15 and its testing applications to three medical domains. In: The Fifth National Conference on Artificial Intelligence, Philadelphia, PA 1986
Wooldridge, M.: An Introduction to Multi-Agent Systems. Wiley, New Jersey (2002)
Langley, P.: Elements of Machine Learning, San Francisco. Morgan Kaufmann Publishers Inc, CA (1995)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Alpaydin, E.: Introduction to Machine Learning (Adaptive Computation and Machine Learning). MIT Press, Massachusetts (2004)
Stichweh, R.: Systems Theory. In: Badie, B.E.A. (ed.) International Encyclopaedia of Political Science. Sage, New York (2011)
Jech, T.: Set Theory, Thrid Millennium edn. Berlin, New York: Springer (2003)
Gegov, A.: Fuzzy Networks for Complex Systems: A Modular Rule Base Approach. Springer, Berlin (2010)
Gegov, N. Petrov, N., Vatchova, B,: Advance modelling of complex processed by rule based networks. In: 5th IEEE International Conference on Intelligent Systems, London (2010)
Gegov, A., Petrov, N., Vatchova, B., Sanders, D.: Advanced modelling of complex processes by fuzzy networks. WSEAS Trans. Circ. Syst. 10(10), 319–330 (2011)
Bengio, Y.: Learning deep architectures for AI. Found. Trends. Mach. Learn. 2(1), 1–127 (2009)
Freedman, D.A.: Statistical Models: Theory and Practice. Cambridge University Press, Cambridge (2005)
Armstrong, J.S.: Illusions in regression analysis. Int. J. Forecast. 28(3), 689 (2012)
Okafor, A.: Entropy Based Techniques with Applications in Data Mining. Florida (2005)
Aitken, A.C.: Statistical Mathematics, 8th edn. Oliver & Boyd (1957)
Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)
Azhagusundari, B., Thanamani, A.S.: Feature selection based on information gain. Int. J. Inno. Tech. Exploring. Eng. 2(2), 18–21 (2013)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)
Yu, H., Yang, J.: A direct LDA algorithm for high diomensional data—with application to face recognition. Pattern Recogn. 34(10), 2067–2069 (2001)
Yates, D. S., David, S. M., Daren, S. S.: The Practice of Statistics, 3rd edn. Freeman (2008)
Deming, W.E.: On probability as a basis for action. Am. Stat. 29(4), 146–152 (1975)
Kerry, Bland, : Statistics notes: the intracluster correlation coefficient in cluster randomisation. Br. Med. J. 316, 1455–1460 (1998)
Kerber, R.: ChiMerge: discretization of numeric attribute. In: Proceeding of the 10th National Conference on Artificial Intelligence (1992)
Ross, T.J.: Fuzzy Logic with Engineering Applications, 2nd edn. Wiley, West Sussex (2004)
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Liu, H., Gegov, A., Cocea, M. (2016). Conclusion. In: Rule Based Systems for Big Data. Studies in Big Data, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-23696-4_9
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DOI: https://doi.org/10.1007/978-3-319-23696-4_9
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