Поддержка принятия решений при анализе эффективности веб-сайта с применением методов Web Usage Mining
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Support decision-making for analyzing the effectiveness of a website using Web Usage Mining methods

idZelenina A.N. Kokorina A.I.   idPetrosov D.A.

UDC 004.891.2
DOI: 10.26102/2310-6018/2022.37.2.019

  • Abstract
  • List of references
  • About authors

In the modern world, one of the most effective methods to maintain the functioning of an organization or business with a view to facilitating development is to design a website and then to employ it to communicate with users and customers. The website helps to systematize all information about the organization, provides a means of e-commerce and gives the opportunity for representatives of the organization and users to communicate with each other to exchange ideas or feedback on products or services. Thus, effectiveness analysis of the website and appropriate decision-making, regarding its optimization and changes to the design, which will allow the company subsequently to achieve its goals, becomes more relevant. In this article, a decision support system was implemented to analyze the effectiveness of a website using Web Usage Mining methods. Statistical methods, which enable performance improvement of the website based on the information received, were chosen as well as data mining methods, in particular, clustering and association rules that are utilized to personalize content and, in the case of selling websites, purchasing offers, which will significantly increase the loyalty of users and customers.

1. Singh D.K. et al. Computational Intelligence in Web Mining. Innovative Trends in Computational Intelligence. 2022:197–215. DOI: 10.1007/978-3-030-78284-9_9.

2. Sharma S. et al. Performance Evaluation of Secure Web Usage Mining Technique to Predict Consumer Behaviour (SWUM-PCB). Intelligent Computing and Networking. 2022;301:136–145. DOI: 10.1007/978-981-16-4863-2_12.

3. Kandpal N., Singh H.P., Shekhawat M.S. Application of web usage mining for administration and improvement of online counseling website. Int J Appl Eng Res. 2019;14(7):1431–1437.

4. Kumar V., Thakur R. S. Web log analysis tools: at a glance. Proceedings of International Conference on Recent Advancement on Computer and Communication. 2018;34:135–142. DOI:10.1007/978-981-10-8198-9_14.

5. Haridasan A.C., Fernando A.G. Online or in-store: unravelling consumer’s channel choice motives. Journal of Research in Interactive Marketing. 2018;12(2):215–230. DOI: 10.1108/JRIM-07-2017-0060.

6. Schröer C., Kruse F., Gómez J. M. A systematic literature review on applying CRISP-DM process model. Procedia Computer Science. 2021;181:526–534. DOI: 10.1016/J.PROCS.2021.01.199.

7. Hypertext Transfer Protocol (HTTP/1.1) RFC7231: Semantics and Content. Internet Engineering Task Force (IETF). Available at: //datatracker.ietf.org/doc/html/rfc7231 (accessed on 15.04.2022).

8. Somyanonthanakul R., Theeramunkong T. Scenario-based Analysis for discovering Relations among Interestingness Measures. Information Sciences. 2022;590:346–385. DOI: 10.1016/j.ins.2021.12.121.

9. Shetty P., Singh S. Hierarchical Clustering: A Survey. International Journal of Applied Research. 2021;7(4):178–181. DOI: 10.22271/ALLRESEARCH.2021.V7.I4C.8484.

10. Jarman A.M. Hierarchical cluster analysis: Comparison of single linkage, complete linkage, average linkage and centroid linkage method. Georgia Southern University. 2020. DOI: 10.13140/RG.2.2.11388.90240.

11. Dinh, D.T., Fujinami, T., & Huynh, V.N. Estimating the optimal number of clusters in categorical data clustering by silhouette coefficient. In International Symposium on Knowledge and Systems Sciences.2019:1–17. DOI: 10.1007/978-981-15-1209-4_1.

12. Sitompul, Bernad J.D., Opim S.S., and Poltak S. Enhancement clustering evaluation result of davies-bouldin index with determining initial centroid of k-means algorithm. Journal of Physics: Conference Series. 2019; 1235(1). DOI: 10.1088/1742-6596/1235/1/012015.

Zelenina Anna Nikolaevna
Candidate of Technical Sciences, Associate Professor

ORCID | eLibrary |

Voronezh institute of high technologies - autonomous non-profit educational organization of higher education

Voronezh, Russian Federation

Kokorina Anastasiia Igorevna

Data Analysis and Machine Learning, The Financial University under the Government of the Russian Federation (FinU or Financial University), Department of Information Technology and Big Data Analysis

Moscow, Russian Federation.

Petrosov David Aregovich
Candidate of Technical Sciences, Associate Professor

WoS | Scopus | ORCID |

Data Analysis and Machine Learning Department, The Financial University under the Government of the Russian Federation (FinU or Financial University), Department of Information Technology and Big Data Analysis

Moscow, Russian Federation.

Keywords: decision support system, web Usage Mining, website, log file, machine learning, clusterization, association rules

For citation: Zelenina A.N. Kokorina A.I. Petrosov D.A. Support decision-making for analyzing the effectiveness of a website using Web Usage Mining methods. Modeling, Optimization and Information Technology. 2022;10(2). Available from: https://moitvivt.ru/ru/journal/pdf?id=1191 DOI: 10.26102/2310-6018/2022.37.2.019 (In Russ).

243

Full text in PDF

Received 23.05.2022

Revised 14.06.2022

Accepted 27.06.2022

Published 27.06.2022