IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v2y2017i3idhcseit1722398.html

Clustering of large datasets using Hadoop Ecosystem

Author

Listed:
  • Mounica B
  • Aditya Srivastava
  • Md.Faisal Alam

Abstract

In today's rapid change of world along with the advancement of technology, the amount of data being generated and used is very high. The rate of data production is very rapid and is not easy to measure. The existing data processing techniques are not capable enough to process data which are so large. K-means is a traditional clustering method which is easy to implement but it converges to local minima from starting position and is sensitive to initial clusters. Hadoop or the Hadoop Distributed File System (HDFS) is a distributed file system which is highly fault tolerant and can be implemented on low cost hardware. It provides complete access to data for any operation and is suitable for applications that needs large data sets. Hadoop is used for parallel processing of large data set in less time.

Suggested Citation

  • Mounica B & Aditya Srivastava & Md.Faisal Alam, 2017. "Clustering of large datasets using Hadoop Ecosystem," 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. 2(3), pages 127-131, June.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i3:id:hcseit1722398
    Note: Article URL: https://ijsrcseit.com/CSEIT1722398
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT1722398
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/paper/CSEIT1722398.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:v2:y2017:i3:id:hcseit1722398. 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.