Issue:Generalized nets and intuitionistic fuzziness as tools for modelling of data mining processes and tools

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to: navigation, search
shortcut
http://ifigenia.org/wiki/issue:nifs/26/4/9-52
Title of paper: Generalized nets and intuitionistic fuzziness as tools for modelling of data mining processes and tools
Author(s):
Krassimir Atanassov
Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, Sofia-1113, Bulgaria,
Prof. Asen Zlatarov University, Bourgas-8000, Bulgaria
kratAt sign.pngbas.bg
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 26 (2020), Number 4, pages 9–52
DOI: https://doi.org/10.7546/nifs.2020.26.4.9-52
Download: Download-icon.png PDF (316  Kb, Info) Download-icon.png
Abstract: The possibilities for using the apparatuses of generalized nets and intuitionistic fuzzy sets as means for modelling and evaluation of Data Mining processes and tools are discussed and illustrated by examples.
Keywords: Data Mining, Generalized net, Intuitionistic fuzzy set.
AMS Classification: 03E72, 68Q85.
References:
  1. Aladjov, H., Atanassov, K. & Shannon, A. (2002). Generalized net model of temporal learning algorithm for artificial neural networks. Proceedings of First International IEEE Conf. on Intelligent Systems, Vol. 1, 190–193.
  2. Alexieva, J., Choy, E. & Koycheva, E. (2007). Review and bibloigraphy on generalized nets theory and applications. In:– A Survey of Generalized Nets (E. Choy, M. Krawczak, A. Shannon and E. Szmidt, Eds.), Raffles KvB Monograph, No. 10, 207–301.
  3. Andonov, V. (2008). On some properties of one Cartesian product over intuitionistic fuzzy sets. Notes on Intuitionistic Fuzzy Sets, 14(1), 12–19.
  4. Andonov, V. (2013). Intuitionistic fuzzy generalized nets with characteristics of the places of Types 1 and 3. Notes on Intuitionistic Fuzzy Sets, 19(3), 99–110.
  5. Andonov, V. (2014). Reduced Generalized Nets with Characteristics of the Places. International Journal “Information Models and Analyses”, 3(2), 113–125.
  6. Andonov, V. (2017). Generalized Nets with Characteristics of the Arcs. Compt. rend. Acad. bulg. Sci., 70(10), 1341–1347.
  7. Andonov, V., & Atanassov, K. (2013). Generalized nets with characteristics of the places. Compt. rend. Acad. bulg. Sci., 66(12), 1673–1680.
  8. Andreev, S., Atanassov, K., & Sotirov, S. (2014). Generalized net model of a social network with intuitionistic fuzzy estimation. Notes on Intuitionistic Fuzzy Sets, 20(3), 72–83.
  9. Andrew, A. (1983). Artificial Intelligence, Abacus Press, Devon.
  10. Angelov, P. (2013). Autonomous Learning Systems, John Wiley & Sons, Chichester.
  11. Angelov, P., Filev, D., & Kasabov, N. (2010). Evolving Intelligent Systems, John Wiley & Sons, Hoboken.
  12. Antonov, A. (2005). Presentation of neuron by generalized net. Issues in the Representation and Processing of Uncertain Imprecise Information: Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets, and Related Topics, Akademicka Oficyna Wydawnictwo EXIT, Warsaw, 1–10.
  13. Antonov, A., & Hadjitodorov, S. (2012). Concurrent algorithm for learning of neural networks. IEEE 6th International Conference “Intelligent Systems” 2012, 225–228.
  14. Atanassov, K. (1981). Algebraic aspect of E–nets. Proc. of Int. Symp. “Automation of Sci. Research”, Varna, October, 143–148. (in Russian).
  15. Atanassov, K. (1983). Intuitionistic fuzzy sets, VII ITKR’s Session, Sofia, June 1983 (Deposed in Central Sci. - Techn. Library of Bulg. Acad. of Sci., 1697/84) (in Bulg.). Reprinted: Int. J. Bioautomation, 2016, 20(S1), S1–S6.
  16. Atanassov K. (1984). Conditions in generalized nets. Proc. of the XIII Spring Conf. of the Union of Bulg. Math., Sunny Beach, April 1984, 219–226.
  17. Atanassov, K. (1985). The generalized nets and the other graphical means for modelling. AMSE Review, 2(1), 59–64.
  18. Atanassov, K. (1986). Intuitionistic fuzzy sets. Fuzzy sets and Systems, 20(1), 87–96.
  19. Atanassov, K. (1986). Generalized nets and the classes of reduced generalized nets resulting from them. AMSE Review, 3(4), 1–6.
  20. Atanassov K. (1987). Generalized index matrices. Comptes rendus de l’Academie Bulgare des Sciences, 40(11), 15–18.
  21. Atanassov, K. (1987). The generalized E–nets – predecessors of the generalized nets. AMSE Review, 5(3), 5–9.
  22. Atanassov, K. (1990). A generalized net, representing the travelling salesman problem. AMSE Review, 14(4), 61–64.
  23. Atanassov, K. (1990). The generalized net which represents all Petri nets. AMSE Review, 12(3), 33–37.
  24. Atanassov, K. (1991). Generalized Nets, World Scientific, Singapore.
  25. Atanassov, K. (1992). Generalized nets and extensions of the travelling salesman problem. AMSE Review, 21(2), 16–26.
  26. Atanassov, K. (1992). Introduction in Generalized Nets Theory, Pontica–Print, Bourgas, (in Bulgarian).
  27. Atanassov, K. (1993). Generalized nets and some travelling salesman problems. In: Applications of Generalized Nets. (K. Atanassov, Ed.). World Scientific, Singapore, 68–81.
  28. Atanassov, K. (1994). Remark on intuitionistic fuzzy expert systems. BUSEFAL, 59, 71–76.
  29. Atanassov, K. (1997). Generalized nets and artificial intelligence. Advances in Modelling & Analysis, B, 37(1–2), 37–51.
  30. Atanassov, K. (1997). Generalized Nets and Systems Theory, Sofia, Academic Publishing House “Prof. M. Drinov”.
  31. Atanassov, K. (1998). Generalized Nets in Artificial Intelligence. Vol. 1: Generalized nets and Expert Systems, “Prof. M. Drinov” Academic Publishing House, Sofia.
  32. Atanassov, K. (1999). Intuitionistic Fuzzy Sets, Springer, Heidelberg.
  33. Atanassov, K. (2000). Generalized net models of special abstract processes. In: Proc. of the Conf. “Bioprocess systems’2000”, 11–13 Sept., Sofia, I.4–I.10.
  34. Atanassov, K. (2001). Generalized nets as tools for modelling in the area of the artificial intelligence. Advanced Studies in Contemporary Mathematics, 3(1), 21–42.
  35. Atanassov, K. (2004). Generalized nets as tools for modelling, optimization and simulation in the area of the Artificial Intelligence. Soft Computing Foundations and Theoretical Aspects (K. Atanassov, O. Hryniewicz, J. Kacprzyk, Eds.), Academicka Oficyna Wydawnicza EXIT, Warszawa, 19–51.
  36. Atanassov, K. (2007). On Generalized Nets Theory, “Prof. M. Drinov” Academic Publishing House, Sofia.
  37. Atanassov, K. (2012). On Intuitionistic Fuzzy Sets Theory, Springer, Berlin.
  38. Atanassov, K. (2014). Index Matrices: Towards an Augmented Matrix Calculus, Springer, Cham.
  39. Atanassov, K. (2015). Intuitionistic fuzzy logics as tools for evaluation of Data Mining processes, Knowledge-Based Systems, 80, 122–130.
  40. Atanassov, K. (2018). Interval-valued intuitionistic fuzzy sets as tools for evaluation of data mining processes. Notes on Intuitionistic Fuzzy Sets, 24(4), 190–202.
  41. Atanassov, K. (2018). n-Dimensional extended index matrices. Advanced Studies in Contemporary Mathematics, 28(2), 245–259.
  42. Atanassov, K. (2020). Generalized Nets and Intuitionistic Fuzziness in Data Mining, “Prof. M. Drinov” Academic Publishing House, Sofia.
  43. Atanassov, K., & Aladjov, H. (2000). Generalized Nets in Artificial Intelligence. Vol. 2: Generalized nets and Machine Learning. “Prof. M. Drinov” Academic Publishing House, Sofia.
  44. Atanassov, K., Chountas, P., Kolev, B. & Sotirova, E. (2006). Generalized net model of a self–developing expert system. Proc. of the 10th international conference on Intuitionistic fuzzy sets, Sofia, 28–29 October, 35–40.
  45. Atanassov, K., Chountas, P., Kolev, B., & Sotirova, E. (2006). Generalized net model of an expert system with temporal components. Advanced Studies in Contemporary Mathematics, 12(2), 255–289.
  46. Atanassov, K., & Dantchev, S. (2007). Generalized net realizations of Kolmogorov’s algorithm. Issues in Intuitionistic Fuzzy sets and Generalized Nets, 4, 65–74.
  47. Atanassov, K., Daskalov, M., Georgiev, P., Kim, S., Kim, Y., Nikolov, N., Shannon, A., & Sorsich, J. (1997) Generalized Nets in Neurology, Academic Publishing House “Prof. M. Drinov”, Sofia.
  48. Atanassov, K., & Dimitrov, E. (1985). On the representation of M–nets by generalized nets. Proc. of the XIV Spring Conf. of the Union of Bulg. Math., Sunny Beach, April 1985, 317–322.
  49. Atanassov, K., & Dimitrov, E. (1987). Theorem for representation of the generalized modification of Petri nets by generalized nets. AMSE Review, 5(1), 8–13.
  50. Atanassov, K., & Dimitrov, E. (1987). Theorem for representation of the Self–modification nets by generalized nets. AMSE Review, 5(3), 1–4.
  51. Atanassov, K., Dincheva, E., Matev, D., & Stefanova-Pavlova, M. (1989). Generalized net representation of flexible manufacturing systems. Methods of Operations Research, Vol. 63., Proc. of the 14-th Symposium on Operations Research. Ulm, Sept., 521–528.
  52. Atanassov, K., & Georgiev, Ch. (1993). Intuitionistic fuzzy Prolog. Fuzzy sets and Systems, 53(1), 121–128.
  53. Atanassov, K.,& Gluhchev, G. (2007). Generalized net model of the automatic natural language translation. Proceedings of the Eighth Int. Workshop on Generalized Nets, Sofia, 26 June 2007, 32–37.
  54. Atanassov, K., Gluhchev, G., Hadjitodorov, S., Kacprzyk, J., Shannon, A., Szmidt, E., & Vassilev, V. (2006). Generalized Nets Decision Making and Pattern Recognition, Warsaw School of Information Technology, Warszawa.
  55. Atanassov, K., Gluhchev, G., Hadjitodorov, S., Shannon, A., & Vasilev, V. (2003). An example of the aplication of generalized nets in Artificial Intelligence. Compt. Rend. de Comptes Rendus de l’Academie bulgare des Sciences, 56(5), 13–18.
  56. Atanassov, K., Gluhchev, G., Hadjitodorov, S., Shannon, A., & Vassilev, V. (2003). Generalized Nets and Pattern Recognition. KvB Visual Concepts Pty Ltd, Monograph No. 6, Sydney, 2003.
  57. Atanassov, K., & Hadjiski, M. (2010). Generalized nets and intelligent systems. Int. Journal of General Systems, 39(5), 457–470.
  58. Atanassov, K., Kacprzyk, A., & Sotirova, E. (2014). A Novel Generalized Net Model of the Executive Compensation Design. Journal of Automation, Mobile Robotics & Intelligent Systems, 8(3), 29–39.
  59. Atanassov, K., Krawczak, M., & Sotirov, S. (2010). Generalized net model for parallel optimization of feed-forward neural network with variable learning rate backpropagation algorithm. Advanced Intelligent systems from theory to practice, Springer, 361–372.
  60. Atanassov, K., Mavrov, D., & Atanassova, V. (2014). Intercriteria decision making: A new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 11, 1–8.
  61. Atanassov, K., Orozova, D., Sotirova, E., Chountas, P., & Tasseva, V. (2007). Generalized net model of expert system validity testing process. Proc. of the international conference of BFU, 165–173.
  62. Atanassov, K., Pasi, G., & Yager, R. (2002). Intuitionistic fuzzy interpretations of multi-measurement tool multi-criteria decision making. Proceedings of the Sixth International Conference on Intuitionistic Fuzzy Sets (J. Kacprzyk and K. Atanassov, Eds.), Varna, 13–14 Sept., Notes on Intuitionistic Fuzzy Sets, 8(3), 66–74.
  63. Atanassov, K., Pasi, G., & Yager, R. (2002). Intuitionistic fuzzy interpretations of multiperson multi-criteria decision making. Proceedings of 2002 First International IEEE Conference on Intelligent Systems, Vol. 1, 115–119.
  64. Atanassov, K., Pasi, G.,& Yager, R. (2005). Intuitionistic fuzzy interpretations of multicriteria multi-person and multi-measurement tool decision making. International Journal of Systems Science, 36(14), 859–868.
  65. Atanassov K., Pasi, G., Yager, R., & Atanassova, V. (2003). Intuitionistic fuzzy graph interpretations of multi-person multi-criteria decision making. Proc. of the Third Conf. of the European Society for Fuzzy Logic and Technology EUSFLAT’ 2003, Zittau, 10–12 Sept. 2003, 177–182.
  66. Atanassov, K., & Pencheva, T. (2013). Generalized net model of simple genetic algorithm modifications. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 10, 97–106.
  67. Atanassov, K., Peneva, D., Taseva, V., Sotirova, E., & Orozova, D. (2006). Generalized net model of expert systems with frame – Type Data Base with intuitionistic fuzzy estimations. First International Workshop on Intuitionistic fuzzy sets, Generalized nets and Knowledge Engineering, London, UK, 2–6 September 2006, 111–116.
  68. Atanassov, K., & Sotirov, S. (2006). Optimization of a neural network of self-organizing maps type with time-limits by a generalized net. Advanced Studies on Contemporary Mathematics, 13(2), 213–220.
  69. Atanassov, K., Sotirov, S., & Antonov, A. (2007). Generalized net model for parallel optimization of feed-forward neural network. Advanced Studies in Contemporary Mathematics, 15(1), 109–119.
  70. Atanassov, K., Sotirov, S., & Shannon, A. (2012). Generalized net model of the hierarchical neural networks. Proceedings of the 13th International Workshop on Generalized Nets, 29 October 2007, London, UK, 8–14.
  71. Atanassov, K., Sotirova, E., & Bureva, V. (2013). On index matrices. Part 4: New operations over index matrices. Advanced Studies in Contemporary Mathematics, 23(3), 547–552.
  72. Atanassov, K., Sotirova, E., & Orozova, D. (2006). Generalized Net Model of Expert Systems with Frame-Type Data Base. Jangjeon Mathematical Society, 9(1), 91–101.
  73. Atanassov, K., Szmidt, E., & Kacprzyk, J. (2013). On intuitionistic fuzzy pairs. Notes on Intuitionistic Fuzzy Sets, 19(3), 1–13.
  74. Atanassov, K., Szmidt, E., Kacprzyk, J., & Atanassova, V. (2015). Intuitionistic fuzzy approach to the preference degree estimations. Comptes Rendus de l’Academie bulgare des Sciences, 68(1), 25–32.
  75. Atanassov, K., Szmidt, E., Kacprzyk, J., & Atanassova, V. (2017). An approach to a constructive simplification of multiagent multicriteria decision making problems via intercriteria analysis. Comptes rendus de l’Academie bulgare des Sciences, 70(8), 1147–1156.
  76. Atanassova, V. (2012). Generalized nets with volumetric tokens. Comptes rendus de l’Academie bulgare des Sciences, 65(11), 1489–1498.
  77. Atanassova, V., & Atanassov, K. (2011). Ant colony optimization approach to tokens’ movement within generalized nets. Lecture Notes in Computer Science, Vol. 6046, Springer, Berlin, 240–247.
  78. Atanassova, V., Doukovska, L., Atanassov, K., & Mavrov, D. (2014). InterCriteria Decision Making approach to EU Member States Competitive Analysis. Proc. of 4th Int. Symposium on Business Modeling and Software Design, Luxembourg, Grand Duchy of Luxembourg, 24–26 June 2014, 289–294.
  79. Atanassova, V., Fidanova, S., Chountas, P., & Atanassov, K. (2012). A generalized net with an ACO-algorithm optimization component, Lecture Notes in Computer Science, Vol. 7116, 190–197.
  80. Atanassova, V., Mavrov, D., Doukovska, L., & Atanassov, K. (2014). Discussion on the threshold values in the InterCriteria Decision Making approach. Notes on Intuitionistic Fuzzy Sets, 20(2), 94–99.
  81. Behera, H.S., Mohapatra, D.P. (Eds.) (2016). Computational Intelligence in Data Mining. Vol. 1, Springer, New Delhi.
  82. Behera, H.S., Mohapatra, D.P. (Eds.) (2016). Computational Intelligence in Data Mining. Vol. 2, Springer, New Delhi.
  83. Berti-Equille, L. (2007). Measuring and modelling data quality for quality-awareness in data mining. In:– Quality Measures in Data Mining (F. Guillet and H. Hamilton, Eds.), Springer, Berlin, 101–126.
  84. Beuerlein, B. et al. (2018). Big Data and the Role of the Actuary. American Academy of Actuaries, Washington, June 2018.
  85. Bonacina, M. (1987). Petri nets for knowledge representation. Petri Nets Newsletter, 27, 28–36.
  86. Boyd, D., & Crawford, K. (2012). Critical questions for Big Data. Information, Communication & Society, 15(5), 662–679.
  87. Bramer, M. (2013). Principles of Data Mining. Springer, London.
  88. Bull, L., Ester, B.-M., & Holmes, J. (2008). Learning classifier systems in data mining: An introduction. In:– Learning Classifier Systems in Data Mining (L. Bull, B.-M. Ester, J. Holmes, Eds.), Springer, Berlin, 1–15.
  89. Bureva, V. (2012). Generalized net model of the creating of associative rules. Annual of “Informatics” Section, Union of Scientists in Bulgaria, 5, 73–83 (in Bulgarian).
  90. Bureva, V. (2014). Intuitionistic fuzzy histograms in grid-based clustering. Notes on Intuitionistic Fuzzy Sets, 20(1), 55–62.
  91. Bureva, V., Chountas, P., & Atanassov, K. (2012). A generalized net model of the process of decision tree construction. 13th Int. Workshop on Generalized Nets, London, 29 October 2012, 1–7.
  92. Bureva, V., & Sotirova, E. (2013). Generalized net of the process of association rules discovery by Eclat algorithm using weather databases. 14-th International Workshop on Generalized Nets, IWGN’2013, Burgas, 29–30 November, 1–10.
  93. Bureva, V., Sotirova, E., & Atanassov, K. (2014). Hierarchical generalized net model of the process of clustering. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 11, 73–80.
  94. Bureva, V., Sotirova, E., & Atanassov, K. (2014). Hierarchical generalized net model of the process of selecting a method for clustering. Proceedings of the 15-th International Workshop on Generalized Nets, 39–48.
  95. Bureva, V., Sotirova, E., & Chountas, P. (2015). Generalized Net of the Process of Sequential Pattern Mining by Generalized Sequential Pattern Algorithm (GSP), Intelligent Systems’2014, Springer, Cham, 831–838.
  96. Bureva, V., Traneva, V., Sotirova, E., & Atanassov, K. (2017). Index matrices and OLAPcube. Part 2: An presentation of the OLAP-analysis by index matrices. Advanced Studies in Contemporary Mathematics, 27 (4), 647–672.
  97. Bureva, V., Traneva, V., Sotirova, E., & Atanassov, K. (2019). Index matrices and OLAPcube. Part 4: A presentation of the OLAP “drill across” operation by index matrices. Advanced Studies in Contemporary Mathematics, 29(1), 109–123.
  98. Bureva, V., Traneva, V., Sotirova, E., & Atanassov, K. (2020). Index matrices and OLAPcube. Part 5: Index matrix operations over OLAP-cube. Advanced Studies in Contemporary Mathematics, 30(1), 69–88.
  99. Buy ¨ uk ¨ ozkan, G., & Feyaio ¨ glu, O. (2005). Accelerating the new product introduction with ˇ intelligent data mining. In:– Intelligent Data Mining: Techniques and Applications (D. Ruan, G. Chen, E. Kerre, G. Wets, Eds.), Springer, Berlin, 2005, 337–354.
  100. Chountas, P., Kolev, B., Rogova, E., Tasseva, V., & Atanassov, K. (2007). Generalized Nets in Artificial Intelligence. Vol. 4: Generalized nets, Uncertain Data and Knowledge Engineering. “Prof. M. Drinov” Academic Publishing House, Sofia.
  101. Chountas, P., Kolev, B., Tasseva, V., & Atanassov, K. (2007). Generalized net model for binary operations over intuitionistic fuzzy OLAP cubes. Proceedings of the Eighth Int. Workshop on Generalized Nets, Sofia, 26 June 2007, 66–72.
  102. Chountas, P., Rogova, E., & Atanassov, K. (2011). The notion of H–IFS: An approach for enhancing the OLAP capabilities in oracle10g, International Journal of Intelligent Systems, 26(3), 262–283.
  103. Chountas, P., Sotirova, E., Kolev, B., & Atanassov, K. (2006). On intuitionistic fuzzy expert systems with temporal components. In:- Computational Intelligence, Theory and Applications, Springer, Berlin, 241–249.
  104. Christov, R., & Atanassov, K. (1993). Generalized nets and labyrinths. In:– Applications of Generalized Nets, Atanassov K. (Ed.). World Scientific, Singapore, 82–84.
  105. Cios, K. J., Pedrycz, W., & Swiniarski, R. (1998). Data Mining Methods for Knowledge Discovery, Kluwer.
  106. Cios, K., Pedrycz, W., Swiniarski, R., & Kurgan, L. (2007). Data Mining. A Knowledge Discovery Approach, Springer, New York.
  107. Cohen, P., Cheyer, A., Wang, M., & Baeg, S. C. (1994). OAA: An Open Agent Architecture. AAAI Spring Symposium.
  108. Cox, E. (2005). Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration, Elsevier, Amsterdam.
  109. Crespi-Reghizzi, S., & Mandrioli, D. (1976). Some algebraic properties of Petri nets, Alma Erequeza, 45(2), 130–137.
  110. Dahan, H., Cohen, Sh., Rokach, L., & Maimon, O. (2014). Proactive Data Mining with Decision Trees, Springer, New York.
  111. Dimitrov, E., & Atanassov, K. (1988). Theorem for representation of M-nets by generalized nets. AMSE Review, 6, 5–12.
  112. Dimitrova, M., Vasilev, K., & Sotirov, S. (2010). Generalized net model of the process of the prognosis biomass accumulation with neural network. Developments in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and related topics. Vol II: Applications, System Research Institute, Polish Academy of Science. Warsaw, 2010, 79–90.
  113. Dorigo, M. (1992). Optimization, Learning and Natural Algorithms. Ph.D. thesis. Politecnico di Milano, Italy (in Italian).
  114. Dorigo, M., & Stutzle, T. (2004). Ant Colony Optimization, MIT Press.
  115. Espinasse, B., Picolet, G., & Chouraqui, E. (1997). Negotiation support systems: a multicriteria and multi-agent approach. European Journal of Operational Research, 103(2), 389–409.
  116. Etzion, T., & Yoeli, M. (1983). Super-nets and their hierarchy. Theoretical Computer Science, 23, 243–272.
  117. Feys, R. (1965). Modal Logics, Gauthier, Paris.
  118. Fidanova, S., & Atanassov, K. (2008). Generalized net models of the process of ant colony optimization with intuitionistic fuzzy estimations. Proceedings of the Ninth International Workshop on Generalized Nets (K, Atanassov and A. Shannon, Eds.), Sofia, 4 July 2008, 1, 41–48.
  119. Fidanova, S., & Atanassov, K. (2008). Generalized net models for the process of hybrid ant colony optimization. Comptes Rendus de l’Academie bulgare des Sciences, 61(12), 1535–1540.
  120. Fidanova, S., & Atanassov, K. (2008). Generalized net models of the process of ant colony optimization. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 7, 108–114.
  121. Fidanova, S., & Atanassov, K. (2010). Generalized net models and intuitionistic fuzzy estimations of the process of ant colony optimization, Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 8, 109–124.
  122. Fidanova, S., & Atanassov, K. (2010). Generalized nets as tools for modelling of the ant colony optimization algorithms. Lecture Notes in Computer Science, Vol. 5910, 326–333.
  123. Fidanova, S., Atanassov, K., & Marinov, P. (2011). Generalized Nets in Artificial Intelligence. Vol. 5: Generalized Nets and Ant Colony Optimization. “Prof. M. Drinov” Academic Publishing House, Sofia.
  124. Fidanova, S., Marinov, P., & Atanassov, K. (2010). Generalized net models of the process of ant colony optimization with different strategies and intuitionistic fuzzy estimations. Proceedings of the Jangjeon Mathematical Society, 13(1), 1–12.
  125. Fidanova, S., Marinov, P. & Atanassov, K. (2014). New evaluations of ant colony optimization start nodes. Control and Cybernetics, 43(3), 471–485.
  126. Fishburn, P. (1971). A comparative analysis of Group Decision Methods. Behavioral Science, 16(6), 538–544.
  127. Freitas, A. A. (2010). A Review of Evolutionary Algorithms for Data Mining. In:– Data Mining and Knowledge Discovery Handbook (O. Maimon and L. Rokach, Eds.), Second Edition, Springer, New York, 371–400.
  128. Genrich, H. (1986). Predicate/transition nets. Lecture Notes in Computer Science, Vol. 254, 207–247.
  129. Genrich, H., & Lautenbach, K. (1979). The analysis of distributed systems by means of predicate/transition nets. Lecture Notes in Computer Science, Vol. 70, 123–146.
  130. Geoffrey F. (2000). Big Data HPC Convergence and a bunch of other things, 02/04/2016, http://www.slideshare.net/Foxsden/big-data-hpc-convergence-and-a-bunchof-other-things. Date, C.J. An Introduction to Database Systems, 7th edn., Reading, MA: Addison-Wesley.
  131. Giordana, A., & Saitta, L. (1985). Modelling production rules by means of predicate/transition networks. Inf. Sciences, 35, 1–41.
  132. Gluhchev, G., Atanassov, K., Hadjitodorov, S., & Shannon, A. (2009). A generalized net model of the process of scene analysis. Cybernetics and Information Technologies, 9(1), 13–17.
  133. Gluhchev, G., Atanassov, K., & Vassilev, V. (2008). A generalized net model of scene analysis process. Advances in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Vol. II: Applications, Academic Publishing House EXIT, Warszawa, 2008, 63–68.
  134. Gochev, V., & Atanassov, K. (2004). Over generalized net reprersentation of one type of fuzzy Petri nets. Advanced Studies on Contemporary Mathematics, 8(1), 59–64.
  135. Gochev, V., Atanassov, K., & Chountas, P. (2003). A generalized net representing the functioning and the results of the work of fuzzy Petri nets. Proc. of the 10th ISPE Int. Conf. on Concurrent Engineering “Advanced Design, Production and Management Systems”, 26–30 July 2003, Madeira, 1009–1012.
  136. Gocheva, P., Sotirov, S., & Gochev, V. (2013). Implementation of generalized nets models of feedforward neural networks. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 10, 125–135.
  137. Grabczewski, K. (2014). Meta-Learning in Decision Tree Induction, Springer, Cham.
  138. Granichin, O., Volkovich, Z., & Toledano-Kitai, D. (2015). Randomized Algorithms in Automatic Control and Data Mining, Springer, Berlin.
  139. Grigoriev, D. (1976). Kolmogorov algorithms are stronger than Turing machines. Investigations on Constructive Mathematics and Mathematical Logic. VIII. – In: Notes on the Leningrad Branch of Steklov Mathematical Institute, 60, 29–36.
  140. Grigorova, G., Vassilev, K., & Sotirov, S. (2013). Generalized net model of the process of the prognosis biomass accumulation using TEMPO–amine metal complexes with neural network. New trend in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Applications. Vol. II, System Research Institute, Polish Academy of Science, Warsaw, 2013, 57–66.
  141. Grosan, V., & Abraham, A. (2011). Intelligent Systems – A Modern Approach, Springer, Berlin.
  142. Grutzner, R. (1982). Entwurtsbegleitende Verhaltensanalyse von Rechnersystemen auf der Basis von M–Netzen, Analyse und Synthese von Rechnersystemen, Problemseminar, Nassau, 1982, Teil 1, 84–88.
  143. Grzymala-Busse, J. W. (2010). Rule induction. In:– Data Mining and Knowledge Discovery Handbook (O. Maimon and L. Rokach, Eds.), Second Edition, Springer, New York, 249–265.
  144. Gyurov, P. (1995). A generalized net representing the functioning of all coloured Petri nets and results of this functioning. Advances in Modelling & Analysis, A, AMSE Press, 26(1), 1–9.
  145. Haddadi, A. (1959). Communication and Cooperation in Agent Systems, Berlin, Springer.
  146. Hadjyisky, L., & Atanassov, K. (1990). Theorem for representation of the neuronal networks by generalized nets. AMSE Review, 12(3), 47–54.
  147. Hadjyisky, L., & Atanassov, K. (1992). A generalized net, representing the elements of one neuron network set. AMSE Review, 14(4), 55–59.
  148. Hadjyisky, L., & Atanassov, K. (1993). Generalized nets representing the elements of neuron networks. In: Applications of Generalized Nets. (K. Atanassov, Ed.). World Scientific, Singapore, 49–67.
  149. Hadjyisky, L., & Atanassov, K. (1993). Intuitionistic fuzzy model of a neural network. BUSEFAL, 54, 36–39.
  150. Hadjyisky, L., & Atanassov, K. (1995). Generalized net model of the intuitionistic fuzzy neural networks. Advances in Modelling & Analysis, AMSE Press, 23(2), 59–64.
  151. Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques, Morgan Kaufmann.
  152. Hand, D., Mannila, H. & Smyth, P. (2001). Principles of Data Mining, MIT Press, Chapter 1 “Introduction”, 7.
  153. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning – Data Mining, Inference and Prediction. Springer, New York, 2001.
  154. Hilderman, R., & Peckham, T. (2007). Statistical methodologies from mining potentially interesting contrast sets. In:– Quality Measures in Data Mining (F. Guillet and H. Hamilton, Eds.), Springer, Berlin, 153–177.
  155. Holmes, D., & Jain, L. (Eds.) (2012). Data Mining: Foundations and Intelligent Paradigms, Vol. 2: Statistical, Bayesian, Time Series and other Theoretical Aspects. Springer, Berlin.
  156. Holmes, D., & Jain, L. (Eds.) (2012). Data Mining: Foundations and Intelligent Paradigms, Vol. 3: Medical, Health, Social, Biological and Other Applications. Springer, Berlin.
  157. Holmes, D., Tweedale, J., & Jain, L. (2012). Data mining techniques in clustering, association and classification. In:– Data Mining: Foundations and Intelligent Paradigms, Vol. 1: Clustering, Association and Classification (D. Holmes and L. jain, Eds.), Springer, Berlin, 1–6.
  158. Hong, T.–P., Chen, C.–H., Wu, Y.–L., & Tseng, V. S. (2008). Fining active membership functions in fuzzy data mining. In:– Data Mining: Foundations and Practice (T. Y. Lin, Y. Xie, A. Wasilewska, C.–J. Liau, Eds.), Springer, Berlin, 179–196.
  159. Honko, P. (2017). Granular-Relational Data Mining, Springer, Cham.
  160. Hwang, C., & Lin, M.J. (1987). Group Decision Making under Multiple Criteria. Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Springer-Verlag.
  161. (1999) Introduction to Data Mining and Knowledge Discovery (Third Edition), Two Crows Corporation, Potomac.
  162. Jain, R., & Pancanti, S. (1999). BIOMETRICS - Personal Verification in Network Society, Kluwer Acad. Publ., Massachusets.
  163. Jensen, K. (1981). Coloured Petri nets and the invariant–method, Theoretical Computer Science, 14(3), 317–336.
  164. Jensen, K. (1992). Coloured Petri nets. EATCS Monographs on Theoretical Computer Science. Vol. 1, Berlin, Springer.
  165. Johnson, N., & Leone. F. (1977). Statistics and Experimental Design, John Wiley & Sons, New York.
  166. Kasabov, N. (2007). Evolving Connectionist Systems, Springer, London.
  167. Kecman, V. (2001). Learning and Soft Computing, MIT Press.
  168. Klose, A., Nurnberger, A., Nauck, D., & Kruse, R. (2001). Data Mining with neuro-fuzzy ¨ models.–In: Data Mining and Computational Intelligence, Springer, Berlin, 1–35.
  169. Klosgen, W., & Zytkow, J. (Eds.) (2002). Handbook of Data Mining and Knowledge Discovery, Oxford University Press New York.
  170. Kolev, B., El-Darzi, E., Sotirova, E., Petronias, I., Atanassov, K., Chountas, P., & Kodogiannis, V. (2006). Generalized Nets in Artificial Intelligence. Vol. 3: Generalized nets, Relational Data Bases and Expert Systems. “Prof. M. Drinov” Academic Publishing House, Sofia.
  171. Kolmogorov, A. (1953). To the definition of an algorithm. Uspekhi Mat. Nauk, 8(4), 175–176.
  172. Kotov, V. (1978). An algebra for parallelism based on Petri nets. Lect. Notes in Comp. Sci., 64, 39–55.
  173. Koutanis, D., & Rasshidi, R. (1987). Petri net representation of rule based expert systems. In: First Annual ESD/SMI Expert Systems Conference, 143–152.
  174. Koycheva, E. (2004). Examples of basic components in the UML, represented by generalized nets. Issues in Intuitionistic Fuzzy Sets and Generalized Nets (K. Atanassov, J. Kacprzyk and M. Krawczak, Eds.), Wydawnictwo WSISiZ, Warszawa, 79–87.
  175. Koycheva, E. (2004). Representability of extended UML sequence diagrams with loops by generalized nets. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, Vol. 2 (K. Atanassov, J. Kacprzyk and M. Krawczak, Eds.), Wydawnictwo WSISiZ, Warszawa, 93–96.
  176. Koycheva, E. (2013). Entwurfsbegleitende Leistungsanalyse mit UML, MARTE und Generalisierten Netzen, Oldenbourg Velag, Munchen.
  177. Krawczak, M. (2003). textitMultilayer Neural Systems and Generalized Net Models, Akademicka Oficyna Wydawnicza EXIT, Warszawa.
  178. Krawczak, M. (2003). Modelling of adjoint neural networks by generalized nets,. 9th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2003, Miedzyzdroje (Poland), 25–28 August 2003, Technology Univ. of Szczecin, 33–42.
  179. Krawczak, M. (2003). Generalized net models of multilayer neural networks. Advanced Studies on Contemporary Mathematics, 7(1), 69–86.
  180. Krawczak, M. (2004). Generalized net modelling concept–neural networks models, computer aiding of social, economical and environment development, Systems Research Institute, Polish Academy of Sciences, Warsaw, 203–216.
  181. Krawczak, M. (2004). On a generalized net model of MLNN simulation. Soft Computing Tools, Techniques and Applications, Academicka Oficyna Wydawnicza EXIT, Warszawa, 157–171.
  182. Krawczak, M. (2004). An example of generalized nets applications to modelling of neural networks simulation, Current Issues in Data and Knowledge Engineering, Academicka Oficyna Wydawnicza EXIT, Warszawa, 297–308.
  183. Krawczak, M. (2005). Generalized net models of MLNN learning algorithms. Lecture Notes in Computer Science, 2 (3697), 25–30.
  184. Krawczak, M. (2005). Modelling of adjoint neural networks by generalized nets. Issues in the Representation and Processing of Uncertain Imprecise Information: Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets, and Related Topics, Akademicka Oficyna Wydawnictwo EXIT, Warszawa, 217–227.
  185. Krawczak, M., & Aladjov, H. (2002). Generalized net model of backpropagation learning algorithm. Proc. of the Third Int. Workshop on Generalized Nets, Sofia, 1 October 2002, 32–36.
  186. Krawczak, M., & Aladjov, H. (2003). Generalized net model of adjoint neural networks. Advanced Studies on Contemporary Mathematics, 7(1), 19–32.
  187. Krawczak, M., Bureva, V., Sotirova, E., & Szmidt, E. (2016). Application of the InterCriteria decision making method to universities ranking. In: K. Atanassov, O. Castillo, J. Kacprzyk, et al. (Eds.): New Developments in Uncertainty Representation and Processing. Advances in Intuitionistic Fuzzy Sets and Generalized Nets, Springer, 365–372.
  188. Krawczak, M., El-Darzi, E., Atanassov, K., & Tasseva, V. (2007). Generalized net for control and optimization of real processes through neural networks using intuitionistic fuzzy estimations, Notes on Intuitionistic Fuzzy Sets, 12(2), 54–60.
  189. Krawczak, M., Sotirov, S., & Atanassov, K. (2010). Multilayer Neural Networks and Generalized Nets, Warsaw School of Information Technology, Warsaw.
  190. Krawczak, M., Sotirov, S., & Sotirova, E. (2012). Generalized net model for parallel optimization of multilayer neural network with time limit. IEEE Intelligent Systems IS’12, 173–177.
  191. Looney, C. G. (1988). Fuzzy Petri nets for rule–based decisionmaking. IEEE Trans. on the System, Man and Cybernetics, 18(1), 178–183.
  192. Lorkowski, J., & Kreinovich, V. (2018). Bounded Rationality in Decision Making Under Uncertainty. S., C.
  193. Maimon, O., & Rokach, L. (2010). Introduction to Knowledge Discovery and Data Mining. In:– Data Mining and Knowledge Discovery Handbook (O. Maimon and L. Rokach, Eds.), Second Edition, Springer, New York, 1–15.
  194. Marinov, M. (2008). Shannon informational approach to intuitionistic fuzzy sets. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 7, 7–9.
  195. Marinov, M. (2012). Shannon approach to intuitionistic fuzzy information definition. Part 2. Notes on Intuitionistic Fuzzy Sets, 18(4), 59–63.
  196. Marinov, M. (2012). Intuitionistic fuzzy load balancing in cloud computing. Notes on Intuitionistic Fuzzy Sets, 18(4), 19–25.
  197. Mengov, G., Pulov, S., Atanassov, K., Georgiev, K., & Trifonov, T. (2003). Modeling neural signals with a generalized net. Advanced Studies on Contemporary Mathematics, 7(2), 155–166.
  198. Merlin, P. (1974). A Study of the Recoverability of Computer Systems, Ph.D. thesis, Univ. of California.
  199. Meyer-Nieberg, S., & Beyer, H.-G. (2007). Self-adaptation in evolutionary algorithms. In:– Parameter Setting in Evolutionary Algorithms (F. Lobo, C. Lima, Z. Michalewicz, Eds.), Studies in Computational Intelligence, No. 54, Springer, Berlin, 47–75.
  200. Montebello, M. (2018). AI Injected e-Learning, Springer, Cham.
  201. Moyle, S. (2010). Collaborative Data Mining. In:– Data Mining and Knowledge Discovery Handbook (O. Maimon and L. Rokach, Eds.), Second Edition, Springer, New York, 1029–1039.
  202. Natkin, S. (1980). Les reseaux de Petri stochastiques et leur application a l’evaluation des systems informatiques, Ph.D. thesis, June 1980.
  203. Nikolov, N. (1995). Generalized nets and semantic networks. Advances in Modelling & Analysis, AMSE Press, 27(1), 19–25.
  204. Nikolova, M., Szmidt, E., & Hadjitodorov, S. (2000). Generalized nets with decision making components. In: Proceedings of International Workshop on Generalized Nets, Sofia, 9 July 2000, 1–5.
  205. Nikolova, N. (1998). Generalized net representation of backpropagation neural networks. Advances in Modelling & Analysis, AMSE Press, 1(1), 27–34.
  206. Nocera, S., Murino M., & Cavallaro F. (2014). On the Perspective of Using Multiple Agent Multi Criteria Decision Making for Determining a Fair Value of Carbon Emissions in Transport Planning, Procedia – Social and Behavioral Sciences, 160, 274–283.
  207. Noe, J. (1975). PRO–nets: for modelling processes and processors, Techn. Rep. 75–07–15, Dept. of Comp. Sci. Univ. of Washington, Seattle.
  208. Nutt, G. (1972). The Formulation and Application of Evaluation Nets, Ph.D. thesis, Comp. Sci. Group, Univ. of Washington, Seattle.
  209. Orozova, D., & Atanassov, K. (2012). Generalized net model of the process of selection and usage of an intelligent e–learning system, Comptes Rendus de l’Academie bulgare des Sciences, 65(5), 591–598.
  210. Orozova, D., & Atanassov, K. (2018). Generalized net model of processes related to Big Data. Comptes rendus de l’Academie bulgare des Sciences, 71(12), 1679–1686.
  211. Orozova, D., & Sotirova, E. (2009). Generalized net model of the applying data mining tools, Proc. of the Tenth International Workshop on Generalized Nets, Sofia, 5 December 2009, 22–26.
  212. Orozova, D., Sotirova, E., & Chountas, P. (2009). Generalized net model of the knowledge discovery in medical databases, Bioautomation, 13(4), 281–288.
  213. Orriols-Puig, A., & Bernardo-Mansilla, E. (2008). Mining imbalanced data with learning classifier systems, In:– Learning Classifier Systems in Data Mining (L. Bull, B.-M. Ester, J. Holmes, Eds.), Springer, Berlin, 123–145.
  214. Parvathi, R., Sotirov, S., Gluhchev, G., & Atanassov, K. (2011). A generalized net model of intuitionistic fuzzy image preprocessing. Comptes Rendus de l’Academie bulgare des Sciences, 64(3), 333–338.
  215. Pasi, G., Atanassov, K., Melo Pinto, P., Yager, R., & Atanassova, V. (2003). Multiperson multi-criteria decision making: intuitionistic fuzzy approach and generalized net model. Proc. of the 10th ISPE Int. Conf. on Concurrent Engineering “Advanced Design, Production and Management Systems”, 26–30 July 2003, Madeira, 1073–1078.
  216. Pasi, G., Yager, R., & Atanassov, K. (2004). Intuitionistic fuzzy graph interpretations of multi–person multi–criteria decision making: generalized net approach. Proceedings of Second International IEEE Conference Intelligent Systems, Varna, 22–24 June 2004, Vol. 2, 434–439.
  217. Pechenizkiy, M., Puuronen, S., & Tsymbal, A. (2008). Does relevance matter to data mining research? In:– Data Mining: Foundations and Practice (T. Y. Lin, Y. Xie, A. Wasilewska, C.-J. Liau, Eds.), Springer, Berlin, 251–275.
  218. Pena-Ayala, A. (Ed.). (2014). Educational Data Mining, Springer, Cham.
  219. Pencheva, T. (2011). Generalized nets model of crossover technique choice in genetic algorithms. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 9, 92–100.
  220. Pencheva, T., Atanassov, K., & Shannon, A. (2009). Modelling of a roulette wheel selection operator in genetic algorithms using generalized nets. International Journal Bioautomation, 13(4), 257–264.
  221. Pencheva, T., Atanassov, K., & Shannon, A. (2009). Modelling of a stochastic universal sampling selection operator in genetic algorithms using generalized nets. In: Proceedings of the 10th International Workshop on Generalized Nets, Sofia, 5 December 2009, 1–7.
  222. Pencheva, T., Atanassov, K., & Shannon, A. (2011). Generalized nets model of offspring reinsertion in genetic algorithms. Annual of “Informatics”, Section of the Union of Scientists in Bulgaria, 4, 29–35.
  223. Pencheva, T., Atanassov, K., & Shannon, A. (2011). Generalized net model of selection function choice in genetic algorithms. In: Recent Advances in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Vol. II: Applications, Warsaw, Systems Research Institute, Polish Academy of Sciences, 193–201.
  224. Pencheva, T., Atanassov, K., & Shannon, A. (2013). Generalized nets model of rank–based fitness assignment in genetic algorithms. In: New Trends in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Vol. II: Applications Warsaw, Systems Research Institute, Polish Academy of Sciences, 127–136.
  225. Pencheva, T., Roeva, O., & Shannon, A. (2008). Generalized net models of crossover operators in genetic algorithms. Proceedings of the Ninth International Workshop on Generalized Nets, Sofia, July 4 2008, 2, 64–70.
  226. Peneva, D., Tasseva, V., Kodogiannis, V., Sotirova, E., & Atanassov, K. (2006). Generalized nets as an instrument for description of the process of expert system construction, IEEE, 760–763.
  227. Petkov, T., & Sotirov, S. (2013). Generalized net model of the cognitive and neural algorithm for adaptive resonance theory 1. Int. J. Bioautomation, 17(4), 207–216.
  228. Petkov, T., & Sotirov, S. (2014). Generalized net model of slow learning algorithm of unsupervised ART2 neural network, IWIFSGN’2013 Twelfth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, Warsaw, 61–70.
  229. Petri, C.–A. (1962) Kommunication mit Automaten, Ph.D.diss., Univ. of Bonn, 1962; Schriften des Inst. fur Instrument. Math., No. 2, Bonn.
  230. Plaginakos, V., Tasoulis, D., & Vrahatis, M. (2008). A review of major application arreas of differential evolution. In :– Advances in Differential Evolution (U. Chakraborty, Ed.), Studies in Computational Intelligence, Vol. 143, Springer, Berlin, 197–238.
  231. Popchev, I., & Peneva, V. (1993). An algorithm for comparison of fuzzy sets. Fuzzy Sets and Systems, 60(1), 59–65.
  232. Popova, B., & Atanassov, K. (1994). Opposite generalized nets. I. Advances in Modelling & Analysis, AMSE Press, 19(2), 15–21.
  233. Popova, B., & Atanassov, K. (1994). Opposite generalized nets. II. Advances in Modelling & Analysis, AMSE Press, 19 (2), 23–28.
  234. Prestwich, S. (2008). The relation between complete and incomlpete search. In :– Hybrid Metaheuristics (C. Blum et al., Eds.), Studies in Computational Intelligence, Vol. 114, Springer, Berlin, 63–83.
  235. Radeva, V., Krawczak, M., & Choy, E. (2002). Review and bibliography on generalized nets theory and applications. Advanced Studies in Contemporary Mathematics, 4(2), 173–199.
  236. Ramchandani, C. (1973). Analysis of Asynchronous Concurrent Systems by Timed Petri Nets, Ph.D. thesis, MIT, Cambridge, Mass., Sept 1973.
  237. Ribaric, J., & Fratic, I. (2005). A biometric verification system based on the fusion of palmprint and face features. 4th Int. Symposium on Image and Signal Processing and Analysis, Croatia.
  238. Riedemann, E., & Mayer, U. (1982). Verallgemeinerte, modifizierte Petri–Netze als Modells fur MIMD–Rechner, Abteilung Informatik, Univ. of Dortmund, Forschungebericht Nr. 143.
  239. Rokach, L. (2010). A survey of clustering algorithms. In:– Data Mining and Knowledge Discovery Handbook (O. Maimon and L. Rokach, Eds.), Second Edition, Springer, New York, 269–298.
  240. Roeva, O. (2014). Bat algorithm in terms of generalized net. Proc. of 15th International Workshop on Generalized Nets, Burgas, 1–6.
  241. Roeva, O., Atanassov, K., & Shannon, A. (2008). Generalized net for selection of genetic algorithm operators. Annual of “Informatics” Section of Union of Scientists in Bulgaria, 1, 117–126.
  242. Roeva, O., & Atanassov, K. (2008). Generalized net model of a modified genetic algorithm. Issues in intuitionistic fuzzy sets and generalized nets, 7, 93–99.
  243. Roeva O., Atanassov, K., & Shannon, A.(2007). Generalized net for evaluation of genetic algorithm fitness function. Proceedings of the Eighth International Workshop on Generalized Nets, Sofia, 26 June 2007, 48–55.
  244. Roeva O., & Melo-Pinto, P. (2013). Generalized net model of Firefly algorithm. Proc. of 14th Int. Workshop on Generalized Nets, Burgas, 29 November 2013, 22–27.
  245. Roeva O., & Michalikova, A. (2013). Generalized net model of intuitionistic fuzzy logic control of genetic algorithm parameters. Notes on Intuitionistic Fuzzy Sets, 19(2), 71–76.
  246. Roeva, O., & Pencheva, T. (2010). Generalized net model of a multi-population genetic algorithm. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, (J. Kacprzyk, M. Krawczak and E. Szmidt, Eds.), Wydawnictwo WSISiZ, Warszawa, 8, 91–101.
  247. Roeva, O., Pencheva, T., & Atanassov, K. (2012). Generalized net of a genetic algorithm with intuitionistic fuzzy selection operator. New Developments in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics, Volume I: Foundations, IBS PAN (Systems Research Institute, Polish Academy of Sciences), Warsaw, 167–178.
  248. Roeva, O., Pencheva, T., Atanassov, K., & Shannon, A. (2010). Generalized net model of selection operator of genetic algorithms. IEEE Int. Conf. on Intelligent Systems, 7–9 July 2010, London, UK, 286–289.
  249. Roeva, O., Pencheva, T., Shannon, A., & Atanassov, K. (2013). Generalized Nets in Artificial Intelligence. Vol. 7: Generalized nets and Genetic Algorithms. “Prof. M. Drinov” Academic Publishing House, Sofia.
  250. Roeva, O., & Shannon, A. (2008). A Generalized net model of mutation operator of the breeder genetic algorithm. Proceedings of the Ninth International Workshop on Generalized Nets, Sofia, 4 July 2008, Vol. 2, 59–63.
  251. Roeva, O., Shannon, A., & Pencheva, T. (2012). Description of simple genetic algorithm modifications using generalized nets. IEEE 6th Int. Conf. IS 2012, Sofia, Bulgaria, Vol. 2, 178–183.
  252. Rud, O. P. (2001). Data Mining Cookbook, John Wiley & Sons, Danvers.
  253. Sanchez, U. R., & Kittler, J. (2000). Fusion of talking face biometric modalities for personal identity verification, Proc. IEEE Conf. on Acoustics, Speech, and Signal Processing, France, 1073–1076.
  254. Savov, M., & Gluhchev, G. (2006). Signature verification via “hand-pen” motion investigation. Proc. of the 6th Int. Conf. on Recent Advances in Soft Computing (RASC 2006), (K. Sirlantzis, Ed.), UK, 490–495.
  255. Scheidat T., & Vielhauer, C. (2005) Multimodal bviometrics for voice and handwritting. Communications and Multimedia Security, 9th IFIP Int. Conf.
  256. Schiffers, M., & Wedde, H. (1978). Analysing program solutions of coordination problem by GP–nets. Math. Foundations of Computer Science (ed. J. Winkowski), Lect. Notes in Comp. Sci., 64, 462–473.
  257. Seifert, J., (2004). Data Mining: An Overview, CRS Report for Congress, Order Code RL31798, Dec 2004.
  258. Shannon, A., Atanassov, K., Orozova, O., Krawczak, M., Sotirova, E., Melo-Pinto, P., Petrounias, I., & Kim, T. (2007). Generalized Nets and Information Flow Within a University. Warsaw School of Information Technology, Warsaw.
  259. Shannon, A., Langova-Orozova, D., Sotirova, E., Petrounias, I., Atanassov, K., Krawczak, M., Melo-Pinto, P., & Kim, T. (2005). Generalized Net Modelling of University Processes. KvB Visual Concepts Pty Ltd, Monograph No. 7, Sydney.
  260. Shannon, A., Sorsich, J., & Atanassov, K. (1996). Generalized Nets in Medicine. Academic Publishing House “Prof. M. Drinov”, Sofia.
  261. Shannon, A., Sorsich, J., Atanassov, K., Nikolov, N., & Georgiev, P. Generalized Nets in General and Internal Medicine, “Prof. M. Drinov”. Academic Publishing House, Sofia, Vol. 1, 1998; Vol. 2, 1999; Vol. 3, 2000.
  262. Shapiro, S. (1979). A stochastic Petri nets with applications to modelling occupancy timed for concurrent task systems. Networks, 9, 375–379.
  263. Shmueli, G., Patel, N., & Bruce, P. (2007). Data Mining for Business Intelligence, John Wiley & Sons, Hoboken.
  264. Simovici, D., & Djeraba, Ch. (2014). Mathematical Tools for Data Mining (Second Edition), Springer, London.
  265. Sinachopoulos, A. (1987). Derivation of a contradiction by resolution using Petri nets. Petri Nets Newsletter, 26, 16–29.
  266. Sotirov, S. (2003). Modeling the algorithm backpropagation for learning of neural networks with generalized nets, Part 1. Proc. of the Fourth Int. Workshop on Generalized Nets, Sofia, 23 Sept. 2003, 61–67.
  267. Sotirov, S. (2006). Generalized net model of the accelerating backpropagation algirithm. Proceedings of the Jangjeon Mathematical Society, 217–225.
  268. Sotirov, S. (2008). Generalized net model of the art neural networks. Part 3. Developments in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and related topics. Vol. II: Applications, System Research Institute, Polish Academy of Science. Warsaw, 257–246.
  269. Sotirov, S. (2010). Generalized net model of the time delay neural network. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 9, 125–131.
  270. Sotirov, S. (2010). Modelling distributed time–delay neural network by generalized net. Developments in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Vol. II: Applications, System Research Institute, Polish Academy of Science, Warsaw, 231–238.
  271. Sotirov, S. (2012). Modeling the backpropagation algorithm of the Elman neural network by generalized net. Proceedings of the 13th International Workshop on Generalized Nets, London, 49–55.
  272. Sotirov, S., & Atanassov, K. (2012). Generalized Nets in Artificial Intelligence. Vol. 6: Generalized Nets and Supervised Neural Networks, “Prof. M. Drinov” Academic Publishing House, Sofia.
  273. Sotirov, S., & Dimitrov, A. (2010). Neural network for defining intuitionistic fuzzy estimation in petroleum recognition. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 8, 74–78.
  274. Sotirov, S., & Kodogiannis, V. (2007). Generalized net model of the Grossberg neural networks. Part 2. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 5, 130–138.
  275. Sotirov, S., & Kodogiannis, V. (2010). Generalized net model of the Elman neural network. Eleventh Int. Workshop on GNs and Second Int. Workshop on GNs, IFSs and KE, London, 9–10 July 2010, 21–26.
  276. Sotirov, S., & Krawczak, M. (2006). Modeling the algorithm backpropagation for learning of neural networks with generalized networks. Part 2. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 3, 65–69.
  277. Sotirov, S., & Krawczak, M. (2006). Modeling the work of self-organizing neural networks with generalized networks. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 3, 57–63.
  278. Sotirov, S., & Krawczak, M. (2008). Generalized net model of the art neural networks, Part 1. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 7, 67–74.
  279. Sotirov, S., & Krawczak, M. (2008). Generalized net model of the art neural networks. Part 2, Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 7, 75–82.
  280. Sotirov, S., & Krawczak, M. (2010). Generalized net model of recurrent neural network. Eleventh Int. Workshop on GNs and Second Int. Workshop on GNs, IFSs and KE, London, 9–10 July 2010, 14–20.
  281. Sotirov, S., & Krawczak, M. (2011). Modelling layered digital dynamic network by a generalized net. Issues in intuitionistic fuzzy sets and generalized nets, 9, 84–91.
  282. Sotirov, S., Krawczak, M., & Atanassov, K. (2010). Generalized net model for parallel optimization of multilayer perceptron with momentum backpropagation algorithm, 5th International IEEE Conference “Intelligent Systems”, London, 281–285.
  283. Sotirov, S., Krawczak, M., & Atanassov, K. (2013). Modelling of brain–state–in–a–box neural network with a generalized net. In:– New Trends in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Vol. 2: Applications, SRI, Polish Academy of Sciences, 153–159.
  284. Sotirov, S., Krawczak, M., & Kodogiannis, V. (2004). Generalized nets model of the Grossberg neural network. Part 1. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 4, 27–34.
  285. Sotirov, S., Krawczak, M., & Kodogiannis, V. (2006). Modeling the work of learning vector quantization neural networks. Proceedings of the Seventh Int. Workshop on Generalized Nets, Sofia, 14–15 July 2006, 39–44.
  286. Sotirov, S., Kukenska, V., Hristova, M., Vardeva, I., Staneva, L., Barzov, J., Dimitrov, S., & Stoqnova, S. (2010). Modeling the nonlinear autoregressive network with exogenous inputs with a generalized net. Developments in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and related topics. Vol. II: Applications, System Research Institute, Polish Academy of Science. Warsaw, 223–230.
  287. Sotirov, S., Orozova, D., & Sotirova, E. (2009). Generalized net model of the process of the prognosis with feedforward neural network. Proc. of the XVI–th International Symposium on Electrical Apparatus and Technologies, SIELA 2009, Vol. 1, 272–278.
  288. Sotirov, S., & Sotirova, E. (2014). Generalized net model of the integrated system for early forest-fire detection. IWIFSGN’2013 Twelfth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, Warsaw, 103–114.
  289. Sotirova, E., Atanassov, K., & Chountas. P. (2006). Generalized nets as tools for modelling of open, hybrid and closed systems: an example with an expert system. Advanced Studies in Contemporary Mathematics, October, 13(2), 221–234.
  290. Sotirova, E., Atanassov, K., & Tasseva, V. (2008). Algorithms for constructing of generalized nets on the base of case study ideology. Advanced Studies in Contemporary Mathematics, 16(1), 83–103.
  291. Sotirova, E., & Orozova, D. (2010). Generalized net model of the phases of the data mining process. Developments in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Vol. II: Applications, Warsaw, Poland, 247–260.
  292. Sotirova, E., Petkov, T., Surchev, S., & Krawczak, M. (2011). Generalized net model of clustering with self organizing map. Developments in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Foundations and Applications, Warsaw, Poland, 239–244.
  293. Spurgin, A., & Petkov, G. (2005). Advances simulator data mining for operators’ performance assessment. In:– Intelligent Data Mining: Techniques and Applications (D. Ruan, G. Chen, E. Kerre, G. Wets, Eds.), Springer, Berlin, 487–514.
  294. Stefanova-Pavlova, M., & Atanassov, K. (1993). Generalized net models for flexible manufacturing systems. In: Applications of Generalized Nets. (K. Atanassov, Ed.), World Scientific, Singapore, 172–207.
  295. Starke, P. (1980). Petri–Netze, Berlin, VEB Deutscher Verlag der Wissenschaften.
  296. Stoeva, S., & Atanassov, K. (1986). Generalized net representation of production systems interpreters. In: Proc. of the Fifteenth Spring Conf. of the Union of Bulg. Math., Sunny Beach, 1986, 456–464.
  297. Surchev, S., & Sotirov, S. (2014). Modeling the process of the color recognition with MLP using symbol visualization. IWIFSGN’2013 Twelfth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, Warsaw, 115–124.
  298. Surchev, S., & Sotirov, S. (2004). Modelling the process of color recognition using multilayer neural network. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 10, 143–151.
  299. Surchev, S., Sotirov, S., & Korneta, W. (2013). Bio-inspired artificial intelligence: A generalized net model of the regularization process in MLP. Int. J. Bioautomation, 17(3), 151–158.
  300. Sumathi, S., & Sivanandam, S. (2006). Introduction to Data Mining and Applications, Berlin.
  301. Traneva, V., Bureva, V., Sotirova, E., & Atanassov, K. (2018). Index matrices and OLAPcube. Part 1: Application of the index matrices to presentation of operations in OLAP-cube. Advanced Studies in Contemporary Mathematics, 27(2), 253–278.
  302. Traneva, V., Bureva, V., Sotirova, E., & Atanassov, K. (2018). Index matrices and OLAPcube. Part 3: A presentation of the OLAP “InterCube Set” and “Data cube” operations by index matrices. Advanced Studies in Contemporary Mathematics, 28(3), 423–448.
  303. Valette, R. (1986). Nets in production systems. –In: Proc. of an Advanced Course “Petri nets: Applications and Relationships to Other Models of Concurrency”, Bad Honnef, in Lecture Notes in Computer Science, Vol. 255, 191–217.
  304. Valette, R., & Bako, B. (1990). Software implementation of Petri nets and compilation of rule–based systems. – In: Proc. of the 11th Int. Conf. on Application and Theory of Petri Nets, Paris, 264–283.
  305. Valk, R. (1977). Self-modifying nets, Inst. fur Informatik, Univ. Hamburg, Bericht ¨ IFI–HH–B–34/77, July 1977.
  306. Vassilev, P., & Atanassov, K. (2019). Modifications and Extensions of Intuitionistic Fuzzy Sets. “Prof. M. Drinov” Academic Publishing House, Sofia.
  307. Voss, K. (1986). Nets in Data Bases. – In: Proc. of an Advanced Course “Petri nets: Applications and Relationships to Other Models of Concurrency”, Bad Honnef, in Lecture Notes in Computer Science, Vol. 255, 97–134.
  308. Wang, J., & Lee, H.S. (2007). Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Computers and Mathematics with Applications, 53(11), 1762–1772.
  309. Witten, H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann.
  310. Xing, B., & Gao, W.-J. (2014). Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms, Springer, Cham.
  311. Yang, H., & Huang, H.J. (2004). The multi-class, multi-criteria traffic network equilibrium and systems optimum problem. Transportation Research Part B: Methodological, 38(1), 1–15.
  312. Yao, Y., Zhong, N., & Zhao, Y. (2008). A conceptual framework of data mining. In:– Data Mining: Foundations and Practice (T.Y.Lin, Y. Xie, A. Wasilewska, C.–J. Liau, Eds.), Springer, Berlin, 501–515.
  313. Yeh, C. H., & Chang, Y. H. (2009). Modeling subjective evaluation for fuzzy group multicriteria decision making. European Journal of Operational Research, 194(2), 464–473.
  314. Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
  315. Zerros, C., & Irani, K. (1977). Colored Petri nets: their properties and applications. Systems Engineering Lab. TR 107, Univ. of Michigan.
  316. Zoteva, D. & Krawczak, M. (2017). Generalized Nets as a Tool for the Modelling of Data Mining Processes. A Survey. Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 13, 1–60.
Citations:

The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.