As of August 2024, International Journal "Notes on Intuitionistic Fuzzy Sets" is being indexed in Scopus.
Please check our Instructions to Authors and send your manuscripts to nifs.journal@gmail.com. Next issue: September/October 2024.

Open Call for Papers: International Workshop on Intuitionistic Fuzzy Sets • 13 December 2024 • Banska Bystrica, Slovakia/ online (hybrid mode).
Deadline for submissions: 16 November 2024.

Issue:Interval-valued intuitionistic fuzzy sets as tools for evaluation of data mining processes

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
(Redirected from Issue:Nifs/24/4/190-202)
Jump to navigation Jump to search
shortcut
http://ifigenia.org/wiki/issue:nifs/24/4/190-202
Title of paper: Interval-valued intuitionistic fuzzy sets as tools for evaluation of data mining processes
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
Intelligent Systems Laboratory, Prof. Asen Zlatarov University, Burgas-8010, Bulgaria
krat@bas.bg
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 24 (2018), Number 4, pages 190–202
DOI: https://doi.org/10.7546/nifs.2018.24.4.190-202
Download:  PDF (164 Kb  Kb, File info)
Abstract: Intuitionistic Fuzzy Sets (IFSs), proposed in 1983, are extensions of fuzzy sets. Some years after their introduction, interval-valued IFSs (IVIFSs) were introduced. During the last 30 years, their properties were studied and these sets were used as tool for evaluation of different objects and processes from the area of the Artificial Intelligence. Short review of these legs of research is offered, with some concrete ideas of possible new directions of study. On this basis, a non-formal discussion is raised on the benefits of applying various elements of IVIFSs as tools

for evaluation of Data Mining processes.

Keywords: Data mining, Interval-valued intuitionistic fuzzy set, Intuitionistic fuzzy set.
AMS Classification: 03E72
References:
  1. Angelov, P., Filev, D., & Kasabov, N. (2010). Evolving Intelligent Systems, John Wiley & Sons, Hoboken.
  2. Atanassov, K. (1987). Generalized index matrices. Compt. Rend. de l’Academie Bulgare des Sciences, 40 (11), 15–18.
  3. Atanassov, K. (1988). Review and new results on intuitonistic fuzzy sets. Preprint IMMFAIS- 1-88, Sofia, 1988. Reprinted: Int. J. Bioautomation, 20 (S1), S7–S16.
  4. Atanassov, K. (1994). Remark on intuitionistic fuzzy expert systems, BUSEFAL, 59, 71–76.
  5. Atanassov, K. (1998). Generalized Nets in Artificial Intelligence. Vol. 1: Generalized Nets and Expert Systems, “Prof. M. Drinov” Academic Publishing House, Sofia.
  6. Atanassov, K. (1999). Intuitionistic Fuzzy Sets: Theory and Applications, Springer, Heldelberg.
  7. Atanassov, K. (2014). Index Matrices: Towards an Augmented Matrix Calculus, Springer, Cham.
  8. Atanassov, K. (2015). Intuitionistic fuzzy logics as tools for evaluation of Data Mining processes, Knowledge-Based Systems, 80, 122–130.
  9. Atanassov, K. (2018). Intercriteria Analysis over Patterns. Learning Systems: From Theory to Practice (V. Sgurev, V. Piuri, V. Jotsov, Eds.), Studies in Computational Intelligence, Springer, Cham, 61–71.
  10. Atanassov, K. (2018). On the Most Extended Modal Operator of First Type over Interval- Valued Intuitionistic Fuzzy Sets. Mathematics, 6, 123; doi:10.3390/math6070123.
  11. Atanassov, K. (2018). Intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets. Advanced Studies in Contemporary Mathematics, 28 (2), 167–176.
  12. Atanassov, K., Atanassova, V., & Gluhchev, G. (2015). InterCriteria Analysis: Ideas and problems. Notes on Intuitionistic Fuzzy Sets, 21 (1), 81–88.
  13. Atanassov, K., & Gargov, G. (1989). Interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31 (3), 343–349.
  14. 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.
  15. Atanassov, K., Sotirov, S., & Kodogiannis, V. (2006). Intuitionistic fuzzy estimations of the Wi-Fi connections, First Int. Workshop on Intuitionistic Fuzzy Sets, Generalized Nets, and Knowledge Engineering, London, 6–7 Sept. 2006, 75–80.
  16. Atanassov, K., Sotirov, S., & Krawczak, M. (2009). Generalized net model of the intuitionistic fuzzy feed forward neural network. Notes on Intuitionistic Fuzzy Sets, 15 (2), 18–23.
  17. 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 lAcademie bulgare des Sciences, 70 (8), 1147–1156.
  18. 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.
  19. Bramer, M. (2013). Principles of Data Mining, Springer, London.
  20. 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.
  21. B¨uy¨uk¨ozkan, G., & Feyaioˇglu, O. (2005). Accelerating the new product introduction with intelligent data mining, In:- Intelligent Data Mining: Techniques and Applications (D. Ruan, G. Chen, E. Kerre, G. Wets, Eds.), Springer, Berlin, 337–354.
  22. 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.
  23. 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.
  24. Cios, K., Pedrycz, W., & Swiniarski, R. (1998). Data Mining Methods for Knowledge Discovery, Kluwer.
  25. Cios, K., Pedrycz, W., Swiniarski, R., & Kurgan, L. (2007). Data Mining. A Knowledge Discovery Approach, Springer, New York.
  26. Cox, E. (2005). Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration, Elsevier, Amsterdam.
  27. Dahan, H., Cohen, S., Rokach, L., & Maimon, O. (2014). Proactive Data Mining with Decision Trees, Springer, New York.
  28. 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.), 2nd Edition, Springer, New York, 371–400.
  29. Granichin, O., Volkovich, Z., & Toledano-Kitai, D. (2015). Randomized Algorithms in Automatic Control and Data Mining, Springer, Berlin.
  30. Grosan, V., & Abraham, A. (2011). Intelligent Systems – A Modern Approach, Springer, Berlin.
  31. Grzymala-Busse, J. W. (2010). Rule induction, In:- Data Mining and Knowledge Discovery Handbook (O. Maimon and L. Rokach, Eds.), 2nd Edition, Springer, New York, 249–265.
  32. Hadjyisky, L., & Atanassov, K. (1993). Intuitionistic fuzzy model of a neural network. BUSEFAL, 54, 36–39.
  33. Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques, Morgan Kaufmann.
  34. Hand, D., Mannila, H., & Smyth, P. (2001). Principles of Data Mining, MIT Press.
  35. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning – Data Mining, Inference and Prediction. Springer, New York.
  36. 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.
  37. 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.
  38. 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.
  39. Kasabov, N. (2007). Evolving Connectionist Systems, Springer, London.
  40. Kecman, V. (2001). Learning and Soft Computing, MIT Press.
  41. Klosgen, W., Zytkow, J. (Eds.) (2002). Handbook of Data Mining and Knowledge Discovery, Oxford University Press, New York.
  42. 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.
  43. 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.), 2nd Edition, Springer, New York, 1–15.
  44. 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.
  45. Moyle, S. (2010). Collaborative Data Mining, In:- Data Mining and Knowledge Discovery Handbook (O. Maimon and L. Rokach, Eds.), 2nd Edition, Springer, New York, 1029–1039.
  46. Orriols-Puig, A., & Bernado-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.
  47. 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.
  48. Pena-Ayala, A. (Ed.) (2014). Educational Data Mining, Springer, Cham.
  49. Rokach, L. (2010). A survey of clustering algorithms, In:- Data Mining and Knowledge Discovery Handbook (O. Maimon and L. Rokach, Eds.), 2nd Edition, Springer, New York, 269–298.
  50. Rud, O. P. (2001). Data Mining Cookbook, John Wiley & Sons, Danvers.
  51. Seifert, J. (2004). Data Mining: An Overview, CRS Report for Congress, Order Code RL31798, Dec. 2004.
  52. Shmueli, G., Patel, N., & Bruce, P. (2007). Data Mining for Business Intelligence, John Wiley & Sons, Hoboken.
  53. Simovici, D., & Djeraba, C. (2014). Mathematical Tools for Data Mining, 2nd Edition, Springer, London.
  54. Sotirov, S. (2007). Determining of intuitionistic fuzzy sets in estimating probability of spam in the e-mail by the help of the neural network, Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 4, 43–47.
  55. Sotirov, S. (2007). Intuitionistic fuzzy estimations for connections of the transmit routines of the bluetooth interface, Advanced Studies in Contemporary Mathematics, 15 (1), 99–108.
  56. Sotirov, S. (2007). Method for determining of intuitionistic fuzzy sets in discovering water floods by neural networks, Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 4, 9–14.
  57. Sotirov, S., & Atanassov, K. (2009). Intuitionistic fuzzy feed forward neural network. Cybernetics and Information Technologies, 9 (2), 62–68.
  58. 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.
  59. 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.
  60. Sotirov, S., Kodogiannis, V., & Elijah Blessing, R. (2006). Intuitionistic fuzzy estimations for connections with Low Rate Wireless personal area networks, First Int. Workshop on on Intuitionistic Fuzzy Sets, Generalized Nets, and Knowledge Engineering, London, 6–7 Sept. 2006, 81–87.
  61. Sotirov, S., Vardeva, I., & Krawczak, M. (2013). Intuitionistic fuzzy multilayer perceptron as a part of integrated systems for early forest-fire detection, Notes on Intuitionistic Fuzzy Sets, 19 (3), 81–89.
  62. 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.
  63. Sumathi, S., & Sivanandam, S. (2006). Introduction to Data Mining and Applications, Berlin.
  64. Valchev, D., Sotirov, S. (2009). Intuitionistic fuzzy detection of signal availability in multipath wireless chanels, Notes on Intuitionistic Fuzzy Sets, 15 (2), 24–29.
  65. Vardeva, I., Sotirov, S. (2009). Intuitionistic fuzzy estimation of damaged packets with multilayer perceptron, Proc. of the Tenth International Workshop on Generalized Nets, IWGN’2009, Sofia, 63–69.
  66. Witten, H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann.
  67. 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.
  68. Zadeh, L. (1965). Fuzzy sets. Information and Control, 8, 338–353.
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.