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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiuming Yu & Meijing Li & Kyung Ah Kim & Jimoon Chung & Keun Ho Ryu, 2016. "Emerging Pattern-Based Clustering of Web Users Utilizing a Simple Page-Linked Graph," Sustainability, MDPI, vol. 8(3), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:3:p:239-:d:65021
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    References listed on IDEAS

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    1. Thomas Gruber, 2007. "Ontology of Folksonomy: A Mash-Up of Apples and Oranges," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 3(1), pages 1-11, January.
    2. Li, Gang & Law, Rob & Vu, Huy Quan & Rong, Jia & Zhao, Xinyuan (Roy), 2015. "Identifying emerging hotel preferences using Emerging Pattern Mining technique," Tourism Management, Elsevier, vol. 46(C), pages 311-321.
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    Cited by:

    1. Alessandro Massaro & Daniele Giannone & Vitangelo Birardi & Angelo Maurizio Galiano, 2021. "An Innovative Approach for the Evaluation of the Web Page Impact Combining User Experience and Neural Network Score," Future Internet, MDPI, vol. 13(6), pages 1-21, May.
    2. Xiaoli Wang & Yun Liu & Yanbing Ju, 2018. "Sustainable Public Procurement Policies on Promoting Scientific and Technological Innovation in China: Comparisons with the U.S., the UK, Japan, Germany, France, and South Korea," Sustainability, MDPI, vol. 10(7), pages 1-27, June.
    3. Isaac Machorro-Cano & Ingrid Aylin Ríos-Méndez & José Antonio Palet-Guzmán & Nidia Rodríguez-Mazahua & Lisbeth Rodríguez-Mazahua & Giner Alor-Hernández & José Oscar Olmedo-Aguirre, 2023. "Medical Opinions Analysis about the Decrease of Autopsies Using Emerging Pattern Mining," Data, MDPI, vol. 9(1), pages 1-14, December.
    4. Ziyun Deng & Tingqin He, 2018. "A Method for Filtering Pages by Similarity Degree based on Dynamic Programming," Future Internet, MDPI, vol. 10(12), pages 1-12, December.
    5. Dongwook Kim & Sungbum Kim, 2017. "The Role of Mobile Technology in Tourism: Patents, Articles, News, and Mobile Tour App Reviews," Sustainability, MDPI, vol. 9(11), pages 1-45, November.

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