IDEAS home Printed from https://ideas.repec.org/a/igg/jirr00/v1y2011i3p1-14.html
   My bibliography  Save this article

Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters

Author

Listed:
  • Wan Maseri Binti Wan Mohd

    (University Malaysia Pahang, Malaysia)

  • A.H. Beg

    (University Malaysia Pahang, Malaysia)

  • Tutut Herawan

    (University Malaysia Pahang, Malaysia)

  • A. Noraziah

    (University Malaysia Pahang, Malaysia)

  • K. F. Rabbi

    (University Malaysia Pahang, Malaysia)

Abstract

K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points.

Suggested Citation

  • Wan Maseri Binti Wan Mohd & A.H. Beg & Tutut Herawan & A. Noraziah & K. F. Rabbi, 2011. "Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 1(3), pages 1-14, July.
    • Handle: RePEc:igg:jirr00:v:1:y:2011:i:3:p:1-14
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jirr00:v:1:y:2011:i:3:p:1-14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.