METHODS OF BUILDING A MODEL OF USER BEHAVIOR

2020;
: 43-51
https://doi.org/10.23939/ujit2020.02.043
Received: October 13, 2020
Accepted: October 25, 2020
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University, Department of Artificial Intelligence

The number of clustering methods and algorithms were analysed and the peculiarities of their application were singled out. The main advantages of density based clustering methods are the ability to detect free-form clusters of different sizes and resistance to noise and emissions, and the disadvantages include high sensitivity to input parameters, poor class description and unsuitability for large data. The analysis showed that the main problem of all clustering algorithms is their scalability with increasing amount of processed data. The main problems of most of them are the difficulty of setting the optimal input parameters (for density, grid or model algorithms), identification of clusters of different shapes and densities (distribution algorithms, grid-based algorithms), fuzzy completion criteria (hierarchical, partition and model-based). Since the clustering procedure is only one of the stages of data processing of the system as a whole, the chosen algorithm should be easy to use and easy to configure the input parameters. Results of researches show that hierarchical clustering methods include a number of algorithms suitable for both small-scale data processing and large-scale data analysis, which is relevant in the field of social networks. Based on the data analysis, information was collected within fill a smart user profile. Much attention is paid to the study of associative rules, based on which an algorithm for extracting associative rules is proposed, which allows to find statistically significant rules and to look only for dependencies defined by a common set of input data, and has high computational complexity if there are many classification rules. An approach has been developed that focuses on creating and understanding models of user behaviour, predicting future behaviour using the created template. Methods of modelling pre-processing of data (clustering) are investigated and regularities of planning of meetings of friends on the basis of the analysis of daily movement of people and their friends are revealed. Methods of creating and understanding models of user behaviour were presented. The k-means algorithm was used to group users to determine how well each object lay in its own cluster. The concept of association rules was introduced; the method of search of dependences is developed. The accuracy of the model was evaluated.

  1. Bonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011). Social Network Analysis and Mining for Business Applications. ACM Transactions on Intelligent Systems and Technology, 1(3), 1–37. https://doi.org/10.1145/1961189.1961194
  2. Hardiman, S. J., & Katzir, L. (2013). Estimating clustering coefficients and size of social networks via random walk. Proceedings of the 22nd International Conference on World Wide Web (WWW'2013), 539–550. https://doi.org/10.1145/2488388.2488436
  3. Hrytsiuk, Yu. I., & Grytsyuk, P. Yu. (2019). The methods of the specified points of the estimates of the parameter of probability distribution of the random variable based on a limited amount of data. Scientific Bulletin of UNFU, 29(2), 141–149. https://doi.org/10.15421/40290229
  4. ISO/IEC TR 24028:2020. Information technology – Artificial intelligence – Overview of trustworthiness in artificial intelligence. International Organization for Standardization and International Electrotechnical Commissio (англ.). May 2020. Retrieved from: https://www.iso.org/obp/ui/#iso:std:77608:en
  5. Jadhav, B. S., Bhosale, D. S., & Jadhav, D. S. (2016). Pattern based topic model for data mining. International Conference on Inventive Computation Technologies (ICICT'2016), 1–6. https://doi.org/10.1109/inventive.2016.7824855
  6. Maulik, U., & Bandyopadhyay, S. (2000). Genetic algorithm-based clustering technique. Pattern Recognition, 33(9), 1455–1465. https://doi.org/10.1016/s0031-3203(99)00137-5
  7. Melnykova, N., Marikutsa, U., & Kryvenchuk, U. (2018). The New Approaches of Heterogeneous Data Consolidation. IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT'2018), 408–411. https://doi.org/10.1109/stc-csit.2018.8526677
  8. Newman, M. E. J. (2003). Mixing patterns in networks. Physical Review E, 67(2), 113–126. https://doi.org/10.1103/physreve.67.026126
  9. Osman, Ahmed M. Shahat. (2019). A Novel Big Data Analytics Framework for Smart Cities. Future Generation Computer Systems, 91, 620–33. https://doi.org/10.1016/j.future.2018.06.046
  10. Ramírez-Rubio, R., Aldape-Pérez, M., Yáñez-Márquez, C., López-Yáñez, I., & Camacho-Nieto, O. (2017). Pattern classification using smallest normalized difference associative memory. Pattern Recognition Letters, 93, 104–112. https://doi.org/10.1016/j.patrec.2017.02.013
  11. Ranjith, K. S., Zhenning, Y., Caytiles, R. D., & Iyengar, N. C. S. N. (2017). Comparative Analysis of Association Rule Mining Algorithms for the Distributed Data. International Journal of Advanced Science and Technology, 102, 49–60. https://doi.org/10.14257/ijast.2017.102.05
  12. Shakhovska, N., Fedushko, S., Greguš ml., M., Melnykova, N., Shvorob, I., & Syerov, Y. (2019). Big Data analysis in development of personalized medical system. Procedia Computer Science, 160, 229–234. https://doi.org/10.1016/j.procs.2019.09.461
  13. Shakhovska, N., Kaminskyy, R., Zasoba, E., & Tsiutsiura, M. (2018). Association Rules Mining in Big Data. International Journal of Computing, 17, 25–32.
  14. Yang, T., Hou, Z., Liang, J., Gu, Y., & Chao, X. (2020). Depth Sequential Information Entropy Maps and Multi-Label Subspace Learning for Human Action Recognition. IEEE Access, 8, 135118–135130. https://doi.org/10.1109/access.2020.3006067
  15. Yang, X., Lin, X., & Lin, X. (2019). Application of Apriori and FP-growth algorithms in soft examination data analysis. Journal of Intelligent & Fuzzy Systems, 37(1), 425–432. https://doi.org/10.3233/jifs-179097