kNNVWC: An efficient k-nearest neighbours approach based on various-widths clustering

Abdulmohsen Almalawi, Adil Fahad, Zahir Tari, Muhammad Aamir Cheema, Ibrahim Khalil

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

    2 Citations (Scopus)


    In this paper, a novel k-NN approach based on Various-Widths Clustering, named kNNVWC, is proposed to efficiently find k-NNs for a query object from a given data set. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. Experimental results demonstrate that kNNVWC performs well compared to state-ofart of k-NN search algorithms.

    Original languageEnglish
    Title of host publicationProceedings of the 2016 IEEE International Conference on Data Engineering (ICDE 2016)
    EditorsMei Hsu, Alfons Kemper, Timos Sellis
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages2
    ISBN (Electronic)9781509020195
    Publication statusPublished - 22 Jun 2016
    EventIEEE International Conference on Data Engineering 2016 - Aalto University School of Business, Helsinki, Finland
    Duration: 16 May 201620 May 2016
    Conference number: 32nd (Proceedings)


    ConferenceIEEE International Conference on Data Engineering 2016
    Abbreviated titleICDE 2016
    OtherThe annual ICDE conference addresses research issues in designing, building, managing, and evaluating advanced data systems and applications. It is a leading forum for researchers, practitioners, developers, and users to explore cutting-edge ideas and to exchange techniques, tools, and experiences.
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