Abstract
Web usage mining is a crucial research area that aims to uncover user behavior patterns from web log data because web usage mining can be used to analyze a website’s usage. This study examined web usage mining to discover online users’ usage patterns and used the results to redesign and improve the government website. This study aims to help online customers obtain a better experience. A dataset was collected from the Metropolitan Electricity Authority (MEA) website. Various algorithms, including association rule mining (Apriori and Frequent Pattern-Growth (FP-Growth)) and sequential pattern mining (Generalized Sequential Pattern (GSP) and Prefix-Span), were used to mine the user usage data. The first 30 frequently visited patterns of web usage mining results were selected for analysis and to develop a prototype. It revealed that most pages were accessed directly. Moreover, most users were interested in alternative energy information, the Fuel Adjustment Charge (at the given time) or the FT rate, the power outage announcement page, and reducing electric expenses information. Furthermore, the analysis indicated that customers were interested in making online processes, such as using the contact-us feature, downloading forms, and calculating their electric expenses. A survey was conducted with 30 participants and the results were compared with the usage data from the weblog. Welch’s t-test was utilized to evaluate the prototype. The findings indicated that the newly implemented website, based on the user usage patterns, was more effective and reduced the user’s usage time.
Supported by AIT and MEA.
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References
Belanche, D., Casaló, L.V., Guinalíu, M.: Website usability, consumer satisfaction and the intention to use a website: the moderating effect of perceived risk. J. Retail. Consum. Serv. 19(1), 124–132 (2012)
Charliepaul, C.K., Gnanadurai, G.I.: Comparison of K-mean algorithm and apriori algorithm-an analysis. Int. J. Eng. Technol. Sci. 1(III) (2014)
Cooley, R., Mobasher, B., Srivastava, J.: Mining: information and pattern discovery on the world wide web. In: Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) (1997)
Elhiber, M.H.A., Abraham, A.: Access patterns in web log data: a review. J. Netw. Innovative Comput. 1(2013), 348–355 (2013)
Facca, F.M., Lanzi, P.L.: Recent developments in web usage mining research. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 140–150. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45228-7_15
Fourie, I., Bothma, T.: Information seeking: an overview of web tracking and the criteria for tracking software. In: Aslib Proceedings. Emerald Group Publishing Limited (2007)
Goebel, M., Gruenwald, L.: A survey of data mining and knowledge discovery software tools. ACM SIGKDD Explor. Newslett. 1(1), 20–33 (1999)
Ibrahim, K.K., Obaid, A.J.: Web mining techniques and technologies: a landscape view. J. Phys: Conf. Ser. 1879, 032125 (2021)
Kumar, B.S., Rukmani, K.V.: Implementation of web usage mining using APRIORI and FP growth algorithms. Int. J. Adv. Netw. Appl. 1(06), 400–404 (2010)
Neelima, G., Rodda, S.: Predicting user behavior through sessions using the web log mining. In: 2016 International Conference on Advances in Human Machine Interaction (HMI), pp. 1–5. IEEE (2016)
Nina, S.P., Rahman, M., Bhuiyan, K.I., Ahmed, K.E.U.: Pattern discovery of web usage mining. In: 2009 International Conference on Computer Technology and Development, vol. 1, pp. 499–503. IEEE (2009)
Patil, S.S., Khandagale, H.P.: Enhancing web navigation usability using web usage mining techniques. Int. Res. J. Eng. Technol. 4(6) (2016)
Pei, J., et al.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: proceedings of the 17th international conference on data engineering, pp. 215–224. IEEE (2001)
Resul, D.A.S., Turkoglu, I., Poyraz, M.: Analyzing of system errors for increasing a web server performance by using web usage mining. IU J. Electr. Electron. Eng. 7(2), 379–386 (2007)
Singh, A.P., Jain, D.R.: A survey on different phases of web usage mining for anomaly user behavior investigation. Int. J. Emerg. Trends Technol. Comput. Sci. 3(3) (2014)
Sisodia, D. S., & Verma, S. (2012, May). Web usage pattern analysis through web logs: A review. In 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE) (pp. 49–53). IEEE
Suadaa, L.H.: A survey on web usage mining techniques and applications. In: 2014 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 39–43. IEEE (2014)
Suharjito, S., Diana, D., Herianto, H.: Implementation of classification technique in web usage mining of banking company. In: 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 211–218 (2016)
Ugras, T., Gülseçen, S., Çubukçu, C., İli Erdoğmuş, İ, Gashi, V., Bedir, M.: Research trends in web site usability: a systematic review. In: Marcus, A. (ed.) DUXU 2016. LNCS, vol. 9746, pp. 517–528. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40409-7_49
Varnagar, C.R., Madhak, N.N., Kodinariya, T.M., Rathod, J.N.: Web usage mining: a review on process, methods and techniques. In: 2013 International Conference on Information Communication and Embedded Systems (ICICES), pp. 40–46. IEEE (2013)
Yuan, F., Wang, L.J., Yu, G.: Study on data preprocessing algorithm in web log mining. In: Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 03EX693), vol. 1, pp. 28–32. IEEE (2003)
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Rawira, P., Esichaikul, V. (2023). Web Usage Mining for Determining a Website’s Usage Pattern: A Case Study of Government Website. In: Anutariya, C., Bonsangue, M.M. (eds) Data Science and Artificial Intelligence. DSAI 2023. Communications in Computer and Information Science, vol 1942. Springer, Singapore. https://doi.org/10.1007/978-981-99-7969-1_7
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