Search result clustering through density analysis based K-medoids method

Hung Hungming, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    After obtaining search results through web search engine, classifying into clusters enables us to quickly browse them. Currently, famous search engines like Google, Bing and Baidu always return a long list of web pages which can be more than a hundred million that are ranked by their relevancies to the search key words. Users are forced to examine the results to look for their required information. This consumes a lot of time when the results come into so huge a number that consisting various kinds. Traditional clustering techniques are inadequate for readable descriptions. In this research, we first build a local semantic thesaurus (L.S.T) to transform natural language into two dimensional numerical points. Second, we analyze and gather different attributes of the search results so as to cluster them through on density analysis based K-Medoids method. Without defining categories in advance, K-Medoids method generates clusters with less susceptibility to noise. Experimental results verify our method's feasibility and effectiveness.

    Original languageEnglish
    Title of host publicationProceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages155-160
    Number of pages6
    ISBN (Print)9781479941735
    DOIs
    Publication statusPublished - 2014 Sep 29
    Event3rd IIAI International Conference on Advanced Applied Informatics, IIAI-AAI 2014 - Kitakyushu
    Duration: 2014 Aug 312014 Sep 4

    Other

    Other3rd IIAI International Conference on Advanced Applied Informatics, IIAI-AAI 2014
    CityKitakyushu
    Period14/8/3114/9/4

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    Keywords

    • Clustering
    • K-Medoids
    • Search result organization

    ASJC Scopus subject areas

    • Information Systems

    Cite this

    Hungming, H., & Watada, J. (2014). Search result clustering through density analysis based K-medoids method. In Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014 (pp. 155-160). [6913285] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2014.41