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Application of Convolution Neural Networks in Web Search Log Mining for Effective Web Document Clustering

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  • Suruchi Chawla

    (Shaheed Rajguru College Delhi University, India)

Abstract

The volume of web search data stored in search engine log is increasing and has become big search log data. The web search log has been the source of data for mining based on web document clustering techniques to improve the efficiency and effectiveness of information retrieval. In this paper Deep Learning Model Convolution Neural Network(CNN) is used in big web search log data mining to learn the semantic representation of a document. These semantic documents vectors are clustered using K-means to group relevant documents for effective web document clustering. Experiment was done on the data set of web search query and associated clicked URLs to measure the quality of clusters based on document semantic representation using Deep learning model CNN. The clusters analysis was performed based on WCSS(the sum of squared distances of documents samples to their closest cluster center) and decrease in the WCSS in comparison to TF.IDF keyword based clusters confirm the effectiveness of CNN in web search log mining for effective web document clustering.

Suggested Citation

  • Suruchi Chawla, 2022. "Application of Convolution Neural Networks in Web Search Log Mining for Effective Web Document Clustering," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 12(1), pages 1-14, January.
    • Handle: RePEc:igg:jirr00:v:12:y:2022:i:1:p:1-14
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.300367
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    References listed on IDEAS

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