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

Xiuming Yu, Meijing Li, Kyung Ah Kim, Jimoon Chung and Keun Ho Ryu
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Xiuming Yu: Database/Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Korea
Meijing Li: Database/Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Korea
Kyung Ah Kim: Department of Biomedical Engineering, College of Medicine, Chungbuk National University, Cheongju, Chungbuk 28644, Korea
Jimoon Chung: Namseoul University, Computer Science, Seoul 331-707, Korea
Keun Ho Ryu: Database/Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Korea

Sustainability, 2016, vol. 8, issue 3, 1-18

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.

Keywords: web usage mining; data mining; association rule mining; frequent pattern mining; emerging patterns; TF-IDF (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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