IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v2y2017i6idhcseit1833137.html

Compressive Study on Various Classification Techniques Used in Data Mining

Author

Listed:
  • Charan Singh Tejavath
  • R. P. Singh

Abstract

Data Mining is a developing field which has pulled in an expansive number of data enterprises because of the colossal volume of data oversaw as of late. Productive data mining requires a decent comprehension of the data mining techniques to enhance business opportunity and to enhance the nature of administration gave. In light of such needs, this paper gives a survey of conventional classification techniques utilized for data mining. Classification is utilized to discover in which assemble every datum occasion is identified with a given dataset. It is utilized for ordering data into various classes as indicated by a few requirements. A few noteworthy sorts of classification calculations including C4.5, ID3, k-closest neighbor classifier, Naive Bayes, SVM, and ANN are utilized for classification. By and large, a classification system takes three methodologies Statistical, Machine Learning and Neural Network for classification. While considering these methodologies this paper gives a comprehensive review of various classification calculations and their highlights and confinements

Suggested Citation

  • Charan Singh Tejavath & R. P. Singh, 2017. "Compressive Study on Various Classification Techniques Used in Data Mining," 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(6), pages 1396-1405, April.
  • Handle: RePEc:jbh:ijsrcs:v2:y2017:i6:id:hcseit1833137
    Note: Article URL: https://ijsrcseit.com/CSEIT1833137
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT1833137
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/paper/CSEIT1833137.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    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:i6:id:hcseit1833137. 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.