Abstract
World Wide Web (WWW) means huge amount of web pages and links that provides massive information for internet users. The growth in websites has become more complexity and size of web contents is more abundance. A web usage mining techniques is used in web server log for extracting a user behavior. Three types of user behavior include frequent user, synthetic user and potential user. The objective of this paper is to predict the potential user and it’s navigation pattern from the web log files. The process of web mining works in three main phases such as data pre-processing, classification of users and pattern discovery. This paper deals with clustering techniques for pattern discovery such as path prediction, page gathering, fuzzy clustering, ant-based clustering and graph partitioning, etc.. Comparative study of these techniques gives best results for predicting future visit of potential user in web server log. Among all clustering algorithm, fuzzy clustering algorithm gives 98% accuracy for predicting potential user navigation pattern which is higher than other techniques.
Similar content being viewed by others
References
Zou, X.: A four-gram unified event model for web mining. Clust. Comput. (2017). doi:10.1007/s10586-017-0988-z
Kosala, R., Blockeel, H.: Web mining research: a survey. ACM Sigk. Explor. Newslett. 2(1), 1–15 (2000)
Hay, B., Wets, G., Vanhoof, K.: Web usage mining by means of multi-dimensional sequence alignment methods. In: Fourth International Workshop on Mining Web Data for Discovering Usage Patterns and Profiles(WEBKDD 2002), pp. 50–65, Springer, New York (2002)
Huiying, Z., Wei, L.: An intelligent algorithm of data pre-processing in Web usage mining. In: Fifth World Congress on Intelligent Control and Automation, WCICA, vol. 4, pp. 3119-3123, IEEE (2004)
Chen, J., Yin, J., Tung, A.K., Liu, B.: Discovering web usage patterns by mining cross-transaction association rules. In: International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2655–2660, IEEE (2004)
Frias-Martinez, E., Karamcheti, V.: A customizable behavior model for temporal prediction for web user sequences. In: Fourth International Workshop on Mining Web Data for Discovering Usage Patterns and Profiles (WEBKDD 2002), pp. 66-85, Springer, New York (2003)
Malarvizhi, S.P., Sathyabhama, B.: Frequent page sets from web log by enhanced weighted association rule mining. Clust. Comput. 19(1), 269–277 (2016)
Zhang, C., Zhuang, L.: New path filling method on data preprocessing in web mining. Comput. Inf. Sci. 1(3), 112 (2008)
Gurusamy, A., Sangaiah, S.: Optimal algorithms for generation of user session sequences using server side web user logs. In: International Conference on Network and Service Security, N2S’09, pp. 1–6, IEEE (2009)
Beemanapalli, K., Rangarajan, R., Srivastava, J.: Incorporating usage information into average-clicks algorithm. In: Eighth International Workshop on Knowledge Discovery on Advances in Web Mining and Web Usage Analysis (WEBKDD 2006), pp. 21–35, Springer, New York (2006)
Kraft, D.H., Chen, J., Mikulcic, A.: Combining fuzzy clustering and fuzzy inference in information retrieval. In: The Ninth IEEE International Conference on Fuzzy Systems FUZZ IEEE, vol. 1, pp. 375–380, IEEE (2000)
Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. ACM 43(8), 142–151 (2000)
Yan, T.W., Jacobsen, M., Garcia-Molina, H., Dayal, U.: From user access patterns to dynamic hypertext linking. Comput. Netw. ISDN Syst. 28(7—-11), 1007–1014 (1996)
Shahabi, C., Zarkesh, A.M., Adibi, J., Shah, V.: Knowledge discovery from users web-page navigation. In: Seventh International Workshop on Research Issues in Data Engineering High Performance Database Management for Large-Scale Applications, pp. 20–29, IEEE (1997)
Yang, H., Parthasarathy, S.: On the use of constrained Aassociations for web log mining. In: Fourth International Workshop on Mining Web Data for Discovering Usage Patterns and Profiles (WEBKDD 2002), pp. 100–118, Springer, New York (2003)
Bose, A., Beemanapalli, K., Srivastava, J., Sahar, S.: Incorporating concept hierarchies into usage mining based recommendation. In: Eighth International Workshop on Knowledge Discovery on Advances in Web Mining and Web Usage Analysis (WEBKDD 2006), pp. 110–126, Springer, New York (2007)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Anandhi, D., Ahmed, M.S.I. Prediction of user’s type and navigation pattern using clustering and classification algorithms. Cluster Comput 22 (Suppl 5), 10481–10490 (2019). https://doi.org/10.1007/s10586-017-1090-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1090-2