An Experimental Study on Clustering Techniques in Data Mining

Authors

  • Hemendra Kumar  Computer Engineering, Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India
  • Krishna Kant Asopa  Computer Engineering, Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India
  • Shruti Bijawat  Computer Engineering, Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India

Keywords:

Data mining, Simple K means, hierarchical clustering, farthest first

Abstract

Clustering is important in data analysis and data mining applications. Cluster can mean as a conglomerate of data sets which can be seen similar to other data set in the same cluster and also are not similar to the different objects in same clusters.[1]The objective of data mining process is to come out with output of useful and relevant information from a large data set and convert it into an understandable form so that it can be used in future. The Aim of this paper is to identify the high-profit, low error, high efficiency and high-value by one of the data mining technique.

References

  1. Dr. Sankar Rajagopal "Customer Data Clustering Using Data Mining Techniques”, vol.3, No.4, Nov. 2011.
  2. Navneet Kaur,Amandeep Kaur Mann, “Survey Paper on Clustering ",april, 2013.
  3. Ashish Dutt, Saeed Aghabozrgi, Maizatulm, Akmal Binti Ismail and Hamidreza Mahroeian, "Clustering algorithms applied in educational data mining", March 2015.
  4. A. Dharmarajan and T. Velmurugan," Lung Cancer Data Analysis by K means and Farthest First Clustering Algorithms",2015
  5. Amjad Abu Saa, “Education Data Mining & Student'Performance Prediction", 2016.

International Journal of Scientific Research in Science and Technology

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Published

2018-03-25

Issue

Volume 4, Issue 6, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Hemendra Kumar, Krishna Kant Asopa, Shruti Bijawat "An Experimental Study on Clustering Techniques in Data Mining" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 4, Issue 6, pp.111-115, March-April-2018.