Web Users Clustering Based on Fuzzy C-MEANS

Waleed Ali, Mohammed Alrabighi

Abstract


The Web contributes greatly to our life in many fields such as education, entertainment, Internet banking, online shopping and software downloading. This has led to rapid growth in the number of Internet users, which resulting in an explosive increase in traffic or bottleneck over the Internet performance. This paper proposes a new approach to group users according to their Web access patterns. The proposed approach for grouping users is based on Fuzzy c-means technique, which allows web users to be assigned into more than one cluster or interest. Each web user has a degree of membership of belonging to each cluster. The experimental results showed that the web users were successfully clustered to similar groups very fast using Fuzzy-c-means.  In addition, the Fuzzy-c-means performed well and became much better when the clusters number increased on two real Bo2 and NY datasets. The proposed intelligent web users clustering based on Fuzzy-c-means can be used for discovering users' interests in Web pages that can contribute in enhancing several approaches such as Web caching, Web pre-fetching and Web recommender systems that are recently used to improve the Web performance.

Full Text:

PDF

References


Koutsonikola, V. A., Vakali, A. I. (2009). A fuzzy bi-clustering approach to correlate web users and pages. International Journal of Knowledge and Web Intelligence, 1(1-2), 3-23.

Liu, B. (2007). Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer Verlag

Eirinaki, M., Vazirgiannis, M. (2003). Web mining for web personalization. ACM Transactions on Internet Technology, 3(1), 1–27.

Frémal, S., Lecron, F. (2017). Weighting strategies for a recommender system using item clustering based on genres. Expert Systems with Applications, 77, 105-113.

Neelima, G., & Rodda, S. (2016). Predicting user behavior through sessions using the web log mining. 2016 International Conference on Advances in Human Machine Interaction (HMI) (pp. 1-5). IEEE.

Maged Mohammed (2016). Web users clustering by using k-means and bottom-up algorithms: Master Thesis. Institute of Science, Banaras Hindu University, India.

Xu, J., & Liu, H. (2010). Web user clustering analysis based on K-Means algorithm. In 2010 International Conference on Information, Networking and Automation (ICINA).

Feng, W., Kazi, T. H., Hu, G. (2012). Web Prefetching by ART1 Neural Network. In Software and Network Engineering (pp. 29-40). Springer Berlin Heidelberg.

Rangarajan, S. K., Phoha, V., Balagani, K., Selmic, R. R., Iyengar, S. S. (2004). Web user clustering and its application to prefetching using ART neural networks. IEEE Computer, 45-62.

Chimphlee, S., Salim, N., Ngadiman, M. S., Chimphlee, W., Srinoy, S. (2006). Rough Sets Clustering and Markov model for Web Access Prediction. In Proceedings of the Postgraduate Annual Research Seminar (pp. 470-475).

Vakali, A., Pallis, G., Angelis, L. (2007). Clustering Web Information Sources.

Bezdek, J. C.(1981). Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press.

Zhang, L., Lu, W., Liu, X., Pedrycz, W., Zhong, C. (2016). Fuzzy c-means clustering of incomplete data based on probabilistic information granules of missing values. Knowledge-Based Systems, 99, 51-70.

Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.

NLANR (2010) National Lab of Applied Network Research (NLANR). Sanitized access logs: http://www.ircache.net/




DOI: http://dx.doi.org/10.21015/vtcs.v11i1.434

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.