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A Method for Filtering Pages by Similarity Degree based on Dynamic Programming

Author

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  • Ziyun Deng

    (College of Economics and Trade, Changsha Commerce & Tourism College, Changsha 410116, China
    National Supercomputing Center in Changsha, Hunan University, Changsha 410116, China)

  • Tingqin He

    (National Supercomputing Center in Changsha, Hunan University, Changsha 410116, China)

Abstract

To obtain the target webpages from many webpages, we proposed a Method for Filtering Pages by Similarity Degree based on Dynamic Programming (MFPSDDP). The method needs to use one of three same relationships proposed between two nodes, so we give the definition of the three same relationships. The biggest innovation of MFPSDDP is that it does not need to know the structures of webpages in advance. First, we address the design ideas with queue and double threads. Then, a dynamic programming algorithm for calculating the length of the longest common subsequence and a formula for calculating similarity are proposed. Further, for obtaining detailed information webpages from 200,000 webpages downloaded from the famous website “www.jd.com”, we choose the same relationship Completely Same Relationship (CSR) and set the similarity threshold to 0.2. The Recall Ratio (RR) of MFPSDDP is in the middle in the four filtering methods compared. When the number of webpages filtered is nearly 200,000, the PR of MFPSDDP is highest in the four filtering methods compared, which can reach 85.1%. The PR of MFPSDDP is 13.3 percentage points higher than the PR of a Method for Filtering Pages by Containing Strings (MFPCS).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:12:p:124-:d:190446
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    References listed on IDEAS

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    1. Gang Kou & Chunwei Lou, 2012. "Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data," Annals of Operations Research, Springer, vol. 197(1), pages 123-134, August.
    2. Lee, Ji-Hyun & Yeh, Wei-Chang & Chuang, Mei-Chi, 2015. "Web page classification based on a simplified swarm optimization," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 13-24.
    3. 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.
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