Search result clustering through density analysis based K-medoids method

Hung Hungming, Junzo Watada

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    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.

    本文言語English
    ホスト出版物のタイトルProceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ155-160
    ページ数6
    ISBN(印刷版)9781479941735
    DOI
    出版ステータスPublished - 2014 9 29
    イベント3rd IIAI International Conference on Advanced Applied Informatics, IIAI-AAI 2014 - Kitakyushu
    継続期間: 2014 8 312014 9 4

    Other

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

    ASJC Scopus subject areas

    • 情報システム

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