DDG-Clustering: A novel technique for highly accurate results

Zahraa Said Abdallah, Mohamed Medhat Gaber

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

    5 Citations (Scopus)

    Abstract

    A key to the success of any clustering algorithm is the similarity measure applied. The similarity among different instances is defined according to a particular criterion. State-of-the-art clustering techniques have used distance, density and gravity measures. Some have used a combination of two. Distance, Density and Gravity clustering algorithm "DDG-Clustering" is our novel clustering technique based on the integration of three different similarity measures. The basic principle is to combine distance, density and gravitational perspectives for clustering purpose. Experimental results illustrate that the proposed method is very efficient for data clustering with acceptable running time.

    Original languageEnglish
    Title of host publicationProceedings of the IADIS European Conference on Data Mining 2009 (ECDM'09)
    Subtitle of host publication Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009, Algarve, Portugal, 18 June - 20 June 2009
    EditorsAjith P. Abraham
    Pages163-167
    Number of pages5
    Publication statusPublished - 2009
    EventIADIS European Conference on Data Mining 2009 - Algarve, Portugal
    Duration: 18 Jun 200920 Jun 2009

    Conference

    ConferenceIADIS European Conference on Data Mining 2009
    Abbreviated titleECDM
    CountryPortugal
    CityAlgarve
    Period18/06/0920/06/09
    OtherPart of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009.

    Keywords

    • Cluster density
    • Cluster gravity
    • Data clustering
    • Data mining
    • K-means clustering

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