Analysis of Data using K-Means Clustering Algorithm with Min Max Function
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : S. Narain Sinha, Ram Lal Yadav|
|DOI : 10.14445/22312803/IJCTT-V58P113|
S. Narain Sinha, Ram Lal Yadav "Analysis of Data using K-Means Clustering Algorithm with Min Max Function". International Journal of Computer Trends and Technology (IJCTT) V58(2):82-84, April 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
The information is currently used for wide range of applications. Data mining is a logical process that is used to search through large amount of data in order to find useful data. Data mining is studied for different databases. For the proper utilization of data the data analytics techniques are applied on the data. Data analytics uses clustering, normalization, etc. Clustering is the process of organizing the objects into groups whose members are similar in some way to others. Lot of work is done in this field by different researchers. In this work the new data analytics technique is proposed. The base technique is modified by the new proposed technique. New technique uses the min max function instead of the scaling. The new technique is proposed, designed, implemented in the R language. The results obtained and analysed. The new proposed technique gives the better and compact clusters.
 Rui Xu, Donald Wunsch, “Survey of Clustering Algorithms”, IEEE Transactions on Neural Networks, VOL. 16, NO. 3, MAY 2005.
 Osama Mahmoud Abu Abbas, “Comparisons between Data Clustering Algorithms”, IAJIT, Vol. 5, No. 3, 2008, p.p: 320-325.
 Nimrat Kaur Sidhu, Rajneet Kaur, “Clustering In Data Mining”, International Journal of Computer Trends and Technology (IJCTT), Volume 4, Issue 4, April 2013
 Dhara Patel, Ruchi Modi, Ketan Sarvakar, “A Comparative Study of Clustering Data Mining: Techniques and Research Challenges”, IJLTEMAS, Volume III, Issue IX, September, 2014.
 Mythili S, Madhiya E, “An Analysis on Clustering Algorithms in Data Mining”, International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 3, Issue. 1, January 2014, pg.334 – 340.
 Mugdha Jain, Chakradhar Verma, “ Adapting k-means for Clustering in Big Data”, International Journal of Computer Applications, (0975 – 8887), Volume 101, No.1, September 2014
 Arpit Bansal, Shalini Goel, “Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining”, International Journal of Computer Applications, Volume 157, No 6, January 2017
 Ohidujjaman, Md. Mizanur Rahman, Ms. Raihana Zannat, “Clustering Algorithm with Asynchronous Programming”, American Journal of Engineering Research (AJER), Volume-6, Issue-8, 2017, pp-286-294.
 Abhilash C B, Sharana Basavanagowda, “A Comparative study on clustering of data using Improved K-means Algorithms”, International Journal of Computer Trends and Technology (IJCTT), Volume 4, Issue 4, April 2013
 Akhilesh Kumar Yadav, Divya Tomar, Sonali Agarwal, “Clustering of Lung Cancer Data Using Foggy K-Means”, International Conference on Recent Trends in Information Technology (ICRTIT), 2013
 Sanjay Chakraborty, Prof. N.K Nigwani and Lop Dey “Weather Forecasting using Incremental K-means Clustering”, 2014
 Chew Li Sa; Bt Abang Ibrahim, D.H., Dahliana Hossain, E., bin Hossin, M., "Student performance analysis system (SPAS)," in Information and Communication Technology for The Muslim World (ICT4M), 2014, vol., no., pp.1-6, 17-18 Nov. 2014
 Abdelghani Bellaachia, Erhan Guven, “Predicting Breast Cancer Survivability Using Data Mining Techniques”, Washington DC, 20052, 2010
 Qasem a. Al-Radaideh, Adel Abu Assaf eman Alnagi, “ Predictiong Stock Prices Using Data Mining Techniques”, The International Arab Conference on Information Technology (ACIT’2013), 2013.
 Jian Di, Xinyue Gou, “Bisecting K-means Algorithm Based on K-valued Self determining and Clustering Center Optimization”, Journal of Computers, Volume 13, Number 6,June2018
Data Mining, K-Means Clustering, Data analytics, normalization, Min Max Function.