IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v3y2018i3idhcseit183376.html

Distributed Data Clustering : A Comparative Analysis

Author

Listed:
  • V. Maria Antoniate Martin
  • K. David
  • B. Merlinsuganthi

Abstract

Distributed computing plays an important role in the Data Mining process. Cluster analysis is one of the most common techniques in data mining. Clustering is a task of grouping a set of objects in such a way that objects is in the same group. Data mining is a function that assigns items in a collection to target categories or classes. There are many different techniques and algorithms are available for distributed data clustering. Cluster analysis itself is not one specific algorithm, but the general task to be solved. Many researchers have proposed clustering algorithms, which work efficiently in the distributed mining. This paper compares the performance of distributed clustering algorithms namely, Distributed k-means algorithm and partition algorithm. In this research paper we have to discuss, the comparative analysis of some of these distributed clustering.

Suggested Citation

  • V. Maria Antoniate Martin & K. David & B. Merlinsuganthi, 2018. "Distributed Data Clustering : A Comparative Analysis," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3(3), pages 860-865, April.
    • Handle: RePEc:jbh:ijsrcs:v3:y2018:i3:id:hcseit183376
    Note: Article URL: https://ijsrcseit.com/CSEIT183376
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT183376
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/paper/CSEIT183376.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:jbh:ijsrcs:v3:y2018:i3:id:hcseit183376. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.