Determining an optimal value of K in K-means clustering

Arshad Muhammad Mehar, Kenan Matawie, Anthony Maeder

    Research output: Chapter in Book / Conference PaperConference Paperpeer-review

    38 Citations (Scopus)

    Abstract

    ![CDATA[Partitioning data into a finite number of k homogenous and separate clusters (groups) without use of prior knowledge is carried out by some unsupervised partitioning algorithm like the k-means clustering algorithm. To evaluate these resultant clusters for finding optimal number of clusters, properties such as cluster density, size, shape and separability are typically examined by some cluster validation methods. Mainly the aim of clustering analysis is to find the overall compactness of the clustering solution, for example variance within cluster should be a minimum and separation between the clusters should be a maximum. In this study, for k-means clustering we have developed a new method to find an optimal value of k number of clusters, using the features and variables inherited from datasets. The new proposed method is based on comparison of movement of objects forward/back from k to k+1 and k+1 to k set of clusters to find the joint probability, which is different from the other methods and indexes that are based on the distance. The performance of this method is also compared with some existing methods through two simulated datasets.]]
    Original languageEnglish
    Title of host publicationProceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine, 18-21 December 2013, Shanghai, China
    PublisherIEEE
    Pages51-55
    Number of pages5
    ISBN (Print)9781479913091
    DOIs
    Publication statusPublished - 2013
    EventIEEE International Conference on Bioinformatics and Biomedicine -
    Duration: 18 Dec 2013 → …

    Conference

    ConferenceIEEE International Conference on Bioinformatics and Biomedicine
    Period18/12/13 → …

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