https://oldena.lpnu.ua/handle/ntb/52484| Title: | Data Stream Online Clustering Based on Fuzzy Expectation-Maximization Approach |
| Authors: | Deineko, Anastasiia Zhernova, Polina Gordon, Boris Zayika, Oleksandr Pliss, Iryna Pabyrivska, Nelya |
| Affiliation: | Kharkiv National University of Radio Electronics Lviv Polytechnic National University Tallinn University of Technology |
| Bibliographic description (Ukraine): | Data Stream Online Clustering Based on Fuzzy Expectation-Maximization Approach / Anastasiia Deineko, Polina Zhernova, Boris Gordon, Oleksandr Zayika, Iryna Pliss, Nelya Pabyrivska // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 171–176. — (Dynamic Data Mining & Data Stream Mining). |
| Bibliographic description (International): | Data Stream Online Clustering Based on Fuzzy Expectation-Maximization Approach / Anastasiia Deineko, Polina Zhernova, Boris Gordon, Oleksandr Zayika, Iryna Pliss, Nelya Pabyrivska // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 171–176. — (Dynamic Data Mining & Data Stream Mining). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | big data dynamic data mining data stream mining computational intelligence EM-algorithm fuzzy clustering Kohonen’s self-learning soft clustering |
| Number of pages: | 6 |
| Page range: | 171-176 |
| Start page: | 171 |
| End page: | 176 |
| Abstract: | In the paper the online fuzzy clustering recurrent procedure has been introduced that allows the forming of hyperellipsoidal clusters with an arbitrary orientation of the axes is proposed. Such clustering system is the generalization of a number of known algorithms, it is intended to solve tasks within the general problems of Data Stream Mining (DSM) and Dynamic Data Mining (DDM), when information is sequentially fed to processing in online mode. |
| URI: | https://ena.lpnu.ua/handle/ntb/52484 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://www.ics.uci.edu/~mlearn/MLRepository.html |
| References (Ukraine): | [1] C. C. Aggarwal, Data Mining. Cham: Springer, Int. Publ., Switzerland, 2015. [2] M. Bramer, Principles of Data Mining. Springer-Verlag London, 2016. [3] A. Bifet, R. Gavaldà, G. Holmes, and B. Pfahringer, Machine Learning for Data Streams with Practical Examples in MOA. The MIT Press, 2018. [4] F. Höppner, F. Klawonn, R. Kruse, and T. Runkler, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. John Wiley & Sons. Chichester, 1999. [5] C. C. Aggarwal and C. K. Reddy, Data Clustering. Algorithms and Application. Boca Raton: CRC Press, 2014. [6] R. Xu and D. C. Wunsch, Clustering. IEEE Press Series on Computational Intelligence. Hoboken, NJ: John Wiley & Sons, Inc., 2009. [7] A. Bifet, Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams, IOS Press, 2010. [8] J. Kacprzyk, and W. Pedrycz, Springer Handbook of Computational Intelligence, Berlin Heidelberg: Springer, Verlag, 2015. [9] K.-L. Du and M. N. S. Swamy, Neural Networks and Statistical Learning. London: Springer-Verlag, 2014. [10] J.-C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, N.Y.: Plenum Press, 1981. [11] Ye. V. Bodyanskiy, A. O. Deineko, and Y. V. Kutsenko, “On-line kernel clustering based on the general regression neural network and T. Kohonen’s self-organizing map,” Automatic Control and Computer Sciences, 51(1), pp. 55-62, 2017. [12] J. Keller, J. C. Bezdek, R. Krishnapuram and N. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. The Handbook of Fuzzy Sets. Kluwer, Dordrecht, Netherlands: Springer, vol. 4, 1999. [13] B. Quost, and T. Denœux “Clustering and classification of fuzzy data using the fuzzy EM algorithm,” Fuzzy Sets and Systems. vol. 286, pp. 134-156, March 2016. [14] J. Yu, Ch. Chaomu, and M. S. Yang, “On convergence and parameter selection of the EM and DA-EM algorithms for Gaussian mixtures,” Pattern Recognition, vol. 77, pp. 188-203, May 2018. [15] X. L. Meng and D. B. Rubin, “Maximum likelihood estimation via the ECM algorithm:a general framework,” Biometrica, vol. 80, рр. 267-278, 1993. [16] Ye. Bodyanskiy, “Computational intelligence techniques for data analysis,” Lecture Notes in Informatics, Bonn: GI, pp. 15 – 36, 2005. [17] Ye. Gorshkov, V. Kolodyaznhiy and Ye., Bodyanskiy, “New recursive learning algorithms for fuzzy Kohonen clustering network,” 17th Int. Workshop on Nonlinear Dynamics of Electronic Systems, Rapperswil, Switzerland, pp. 58-61, 2009. [18] L. Jain and C. Mumford, Computational Intelligence, Collaboration, Fuzzy and Emergence, Berlin: Springer, Vergal, 2009. [19] S. Osowski, Sieci neuronowe do przetwarzania informacji, Warszawa: Oficijna Wydawnicza Politechniki Warszawskiej, 2006. [20] A. B. Geva and I. Gath “Unsupervised optimal fuzzy clustering,” Pattern Analysis and Machine Intelligence, vol. 2, n.7, pp. 773-787, 1989. [21] T. Kohonen, Self-Organizing Maps. Berlin: Springer-Verlag, 1995. [22] Ye. Bodyanskiy, A. Deineko, Y. Kutsenko and O. Zayika, “Data streams fast EM-fuzzy clustering based on Kohonen`s self-learning,” 1th IEEE International Conference on Data Stream Mining & Processing (DSMP 2016), Lviv, Ukrane, pp. 309-313, 2016. [23] A. B. Geva, “Clustering as a basis for evolving neuro-fuzzy modeling,” Evolving Systems, pp. 59-71, 2010. [24] UCI Repository of machine learning databases. CA: University of California, Department of Information and Computer Science. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html |
| References (International): | [1] C. C. Aggarwal, Data Mining. Cham: Springer, Int. Publ., Switzerland, 2015. [2] M. Bramer, Principles of Data Mining. Springer-Verlag London, 2016. [3] A. Bifet, R. Gavaldà, G. Holmes, and B. Pfahringer, Machine Learning for Data Streams with Practical Examples in MOA. The MIT Press, 2018. [4] F. Höppner, F. Klawonn, R. Kruse, and T. Runkler, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. John Wiley & Sons. Chichester, 1999. [5] C. C. Aggarwal and C. K. Reddy, Data Clustering. Algorithms and Application. Boca Raton: CRC Press, 2014. [6] R. Xu and D. C. Wunsch, Clustering. IEEE Press Series on Computational Intelligence. Hoboken, NJ: John Wiley & Sons, Inc., 2009. [7] A. Bifet, Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams, IOS Press, 2010. [8] J. Kacprzyk, and W. Pedrycz, Springer Handbook of Computational Intelligence, Berlin Heidelberg: Springer, Verlag, 2015. [9] K.-L. Du and M. N. S. Swamy, Neural Networks and Statistical Learning. London: Springer-Verlag, 2014. [10] J.-C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, N.Y., Plenum Press, 1981. [11] Ye. V. Bodyanskiy, A. O. Deineko, and Y. V. Kutsenko, "On-line kernel clustering based on the general regression neural network and T. Kohonen’s self-organizing map," Automatic Control and Computer Sciences, 51(1), pp. 55-62, 2017. [12] J. Keller, J. C. Bezdek, R. Krishnapuram and N. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. The Handbook of Fuzzy Sets. Kluwer, Dordrecht, Netherlands: Springer, vol. 4, 1999. [13] B. Quost, and T. Denœux "Clustering and classification of fuzzy data using the fuzzy EM algorithm," Fuzzy Sets and Systems. vol. 286, pp. 134-156, March 2016. [14] J. Yu, Ch. Chaomu, and M. S. Yang, "On convergence and parameter selection of the EM and DA-EM algorithms for Gaussian mixtures," Pattern Recognition, vol. 77, pp. 188-203, May 2018. [15] X. L. Meng and D. B. Rubin, "Maximum likelihood estimation via the ECM algorithm:a general framework," Biometrica, vol. 80, rr. 267-278, 1993. [16] Ye. Bodyanskiy, "Computational intelligence techniques for data analysis," Lecture Notes in Informatics, Bonn: GI, pp. 15 – 36, 2005. [17] Ye. Gorshkov, V. Kolodyaznhiy and Ye., Bodyanskiy, "New recursive learning algorithms for fuzzy Kohonen clustering network," 17th Int. Workshop on Nonlinear Dynamics of Electronic Systems, Rapperswil, Switzerland, pp. 58-61, 2009. [18] L. Jain and C. Mumford, Computational Intelligence, Collaboration, Fuzzy and Emergence, Berlin: Springer, Vergal, 2009. [19] S. Osowski, Sieci neuronowe do przetwarzania informacji, Warszawa: Oficijna Wydawnicza Politechniki Warszawskiej, 2006. [20] A. B. Geva and I. Gath "Unsupervised optimal fuzzy clustering," Pattern Analysis and Machine Intelligence, vol. 2, n.7, pp. 773-787, 1989. [21] T. Kohonen, Self-Organizing Maps. Berlin: Springer-Verlag, 1995. [22] Ye. Bodyanskiy, A. Deineko, Y. Kutsenko and O. Zayika, "Data streams fast EM-fuzzy clustering based on Kohonen`s self-learning," 1th IEEE International Conference on Data Stream Mining & Processing (DSMP 2016), Lviv, Ukrane, pp. 309-313, 2016. [23] A. B. Geva, "Clustering as a basis for evolving neuro-fuzzy modeling," Evolving Systems, pp. 59-71, 2010. [24] UCI Repository of machine learning databases. CA: University of California, Department of Information and Computer Science. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Deineko_A-Data_Stream_Online_Clustering_171-176.pdf | 357.52 kB | Adobe PDF | View/Open | |
| 2018_Deineko_A-Data_Stream_Online_Clustering_171-176__COVER.png | 525.93 kB | image/png | View/Open |
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