Notification: Our email services are now fully restored after a brief, temporary outage caused by a denial-of-service (DoS) attack. If you sent an email on Dec 6 and haven't received a response, please resend your email.
CFP last date
20 December 2024
Reseach Article

Improving the Cluster Efficiency on Sea Level Rise Dataset using Data Discretization

by Sharon Dominick, T. Abdul Razak
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 13
Year of Publication: 2014
Authors: Sharon Dominick, T. Abdul Razak
10.5120/17875-8856

Sharon Dominick, T. Abdul Razak . Improving the Cluster Efficiency on Sea Level Rise Dataset using Data Discretization. International Journal of Computer Applications. 102, 13 ( September 2014), 15-18. DOI=10.5120/17875-8856

@article{ 10.5120/17875-8856,
author = { Sharon Dominick, T. Abdul Razak },
title = { Improving the Cluster Efficiency on Sea Level Rise Dataset using Data Discretization },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 13 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number13/17875-8856/ },
doi = { 10.5120/17875-8856 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:01.147937+05:30
%A Sharon Dominick
%A T. Abdul Razak
%T Improving the Cluster Efficiency on Sea Level Rise Dataset using Data Discretization
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 13
%P 15-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rising sea levels, an effect of global warming, is a cause of concern and it is likely to affect the developing countries. With respect to the data set published for research at the World Bank, clustering a data mining technique is applied to detect the most likely to be affected regions. When tested with the k-Means clustering technique, the result of the clustering process reveals a lot of imperfections; this research analyzes the use of data discretization to improve the quality of the clustering process.

References
  1. http://www. nrdc. org/globalwarming/qthinice. asp.
  2. http://www. ucsusa. org/global_warming/science_and_impacts/impacts/infographic-sea-level-rise-global-warming. html.
  3. http://econ. worldbank. org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:22185588~pagePK:64214825~piPK:64214943~theSitePK:469382,00. htm.
  4. Alsabti, Khaled; Ranka, Sanjay; and Singh, Vineet, "An efficient k-means clustering algorithm". Electrical Engineering and Computer Science. 1993.
  5. Fang-Xiang Wu, W. J. Zhang and Anthony J. Kusalik,"A Genetic K-Means Clustering Algorithm applied to Gene Expression Data", Springer-Verlag Berlin Heidelberg. 2003.
  6. Ville Hautam¨aki, Svetlana Cherednichenko, Ismo K¨arkk¨ainen,Tomi Kinnunen, and Pasi Fr¨anti,"Improving K-Means by Outlier Removal", Springer-Verlag Berlin Heidelberg. 2005.
  7. Arthur, D. and Vassilvitskii, S. "k-means++: the advantages of careful seeding". Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Philadelphia, USA. 2007.
  8. Arun Prabha,R. Saranya,"Refinement of K-Means Clustering using Genetic Algorithm", Journal of Computer Applications(JCA),2011.
  9. Jiawei Han, Micheline Kamber, and Jian Pei,"Data mining Concepts and Techniques", 2nd ed, Morgan Kaufmann,2006.
  10. http://www. improvedoutcomes. com/docs/WebSiteDocs/Classification_and_Prediction/SLAM/Discretization. htm
  11. http://www. nrdc. org/globalwarming/fcons/fcons4. asp.
Index Terms

Computer Science
Information Sciences

Keywords

Discretization Clustering Partitioning vulnerable.