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Sustainability 2016, 8(3), 239; doi:10.3390/su8030239

Emerging Pattern-Based Clustering of Web Users Utilizing a Simple Page-Linked Graph

1
Database/Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Korea
2
Department of Biomedical Engineering, College of Medicine, Chungbuk National University, Cheongju, Chungbuk 28644, Korea
3
Namseoul University, Computer Science, Seoul 331-707, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 23 December 2015 / Revised: 29 February 2016 / Accepted: 1 March 2016 / Published: 3 March 2016
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Abstract

Web usage mining is a popular research area in data mining. With the extensive use of the Internet, it is essential to learn about the favorite web pages of its users and to cluster web users in order to understand the structural patterns of their usage behavior. In this paper, we propose an efficient approach to determining favorite web pages by generating large web pages, and emerging patterns of generated simple page-linked graphs. We identify the favorite web pages of each user by eliminating noise due to overall popular pages, and by clustering web users according to the generated emerging patterns. Afterwards, we label the clusters by using Term Frequency-Inverse Document Frequency (TF-IDF). In the experiments, we evaluate the parameters used in our proposed approach, discuss the effect of the parameters on generating emerging patterns, and analyze the results from clustering web users. The results of the experiments prove that the exact patterns generated in the emerging-pattern step eliminate the need to consider noise pages, and consequently, this step can improve the efficiency of subsequent mining tasks. Our proposed approach is capable of clustering web users from web log data. View Full-Text
Keywords: web usage mining; data mining; association rule mining; frequent pattern mining; emerging patterns; TF-IDF web usage mining; data mining; association rule mining; frequent pattern mining; emerging patterns; TF-IDF
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yu, X.; Li, M.; Kim, K.A.; Chung, J.; Ryu, K.H. Emerging Pattern-Based Clustering of Web Users Utilizing a Simple Page-Linked Graph. Sustainability 2016, 8, 239.

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