Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Review of the Fuzzy Web Mining

Author : Dhruv Kumar 1

Date of Publication :7th March 2018

Abstract: Internet has become a boundless asset of knowledge, and hence broadly utilized in numerous applications. Web mining is an application of the data mining methods to find patterns from the WWW. As name proposes, it is data accumulated by mining web. It makes use of robotized apparatus to uncover and extract information from web 2 reports and servers, and it licenses associations to find both unstructured and organised data from server logs, link and website structure, different sources, browser activities, and page content. Web mining assumes a significant job in finding such information. This mining could be generally separated into three classifications, including web content mining, web structure mining and web usage mining. Knowledge and data on Web may, be that as it may, comprise of incomplete, uncertain and imprecise data. Since theory of fuzzy set is regularly used to deal with such information, a few techniques of fuzzy web mining have been proposed for uncover linguistic and fuzzy knowledge. This paper surveys these techniques as per the three classifications of web mining above— web content, web structure mining and web usage mining. Few representative approaches in every class are presented and thought about

Reference :

    1. S. Wang and W. Shi, “Data mining and knowledge discovery,” in Springer Handbook of Geographic Information, 2012.
    2. M. Tsytsarau and T. Palpanas, “Survey on mining subjective data on the web,” Data Min. Knowl. Discov., 2012.
    3.  “Web data mining: exploring hyperlinks, contents, and usage data,” Choice Rev. Online, 2012.
    4. K. Khan, B. Baharudin, A. Khan, and A. Ullah, “Mining opinion components from unstructured reviews: A review,” Journal of King Saud University - Computer and Information Sciences. 2014.
    5. J. Serrano-Guerrero, J. A. Olivas, F. P. Romero, and E. Herrera-Viedma, “Sentiment analysis: A review and comparative analysis of web services,” Inf. Sci. (Ny)., 2015. 
    6.  S. Ghosh, S. Roy, and S. K. Bandyopadhyay, “A tutorial review on Text Mining Algorithms,” Int. J. Adv. Res. Comput. Commun. Eng., 2012.
    7. C. Romero, P. G. Espejo, A. Zafra, J. R. Romero, and S. Ventura, “Web usage mining for predicting final marks of students that use Moodle courses,” Comput. Appl. Eng. Educ., 2013.
    8. M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich, “A review of relational machine learning for knowledge graphs,” Proceedings of the IEEE. 2016. 
    9.  R. Irfan et al., “A survey on text mining in social networks,” Knowledge Engineering Review. 2015.

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