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Reseach Article

K-Means Clustering in Spatial Data Mining using Weka Interface

Published on August 2012 by Ritu Sharma(sachdeva), M. Afshar Alam, Anita Rani
International Conference on Advances in Communication and Computing Technologies 2012
Foundation of Computer Science USA
ICACACT - Number 1
August 2012
Authors: Ritu Sharma(sachdeva), M. Afshar Alam, Anita Rani
7181cff1-fbd5-44d6-9aaa-a32a924e7bef

Ritu Sharma(sachdeva), M. Afshar Alam, Anita Rani . K-Means Clustering in Spatial Data Mining using Weka Interface. International Conference on Advances in Communication and Computing Technologies 2012. ICACACT, 1 (August 2012), 26-30.

@article{
author = { Ritu Sharma(sachdeva), M. Afshar Alam, Anita Rani },
title = { K-Means Clustering in Spatial Data Mining using Weka Interface },
journal = { International Conference on Advances in Communication and Computing Technologies 2012 },
issue_date = { August 2012 },
volume = { ICACACT },
number = { 1 },
month = { August },
year = { 2012 },
issn = 0975-8887,
pages = { 26-30 },
numpages = 5,
url = { /proceedings/icacact/number1/7970-1006/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Communication and Computing Technologies 2012
%A Ritu Sharma(sachdeva)
%A M. Afshar Alam
%A Anita Rani
%T K-Means Clustering in Spatial Data Mining using Weka Interface
%J International Conference on Advances in Communication and Computing Technologies 2012
%@ 0975-8887
%V ICACACT
%N 1
%P 26-30
%D 2012
%I International Journal of Computer Applications
Abstract

Clustering techniques have a wide use and importance nowadays and this importance tends to increase as the amount of data grows. K-means is a simple technique for clustering analysis. Its aim is to find the best division of n entities into k groups (called clusters), so that total distance between the group's members and corresponding centroid, irrespective of the group is minimized. Each entity belongs to the cluster with the nearest mean. It results into a partitioning of the data space into Voronoi Cells. This paper is about the implementation of k-means clustering using crop yield records by Weka Interface. The data has been taken from the website "Agricultural Statistics of India". This papers also includes detailed result analysis of rice data after demonstration via Weka Interface.

References
  1. Privacy-Preserving K-Means clustering over vertically Partitioned Data- By Jaideep Vaidya and Chris Clifton, Deptt. Of Computer Sciences, Purdue University, 250 N University St, West Lafayette, IN 47907-2066
  2. K-means clustering via Principal component analysis – By Chris Ding and Xinofeng He, Computational research division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, Preceeding of 21st International conference on Machine Learning, Banff, Canada, 2004.
  3. K-means clustering Tutorial- By Kardi Teknomo, Ph. D
  4. Application of spatial data mining for Agriculture- By D. Rajesh, AP-SITE, VIT University, Vellore-14, International Journal of computer applications(0975- 8887), Vloume 15-No. 2, February 2011
  5. A hybridized k-means clustering approach for high dimensional dataset- By Rajashree Dash, Debahuti Mishra, Amiya Kumar Rath, Milu Acharya, Orissa, International Journal of Engineering, Science and technology, Vol 2, No. 2, 2010, pp. 59-66
  6. K-means encyclopedia
  7. K-Means clustering using Weka Interface- By Sapna Jain ,M Afshar Aalam and M. N Doja, Jamia Hamdard University, New Delhi, Proceedings of the 4th National Conference; INDIACom-2010 Computing For Nation Development, February 25 – 26, 2010 Bharati Vidyapeeth's Institute of Computer Applications and Management, New Delhi
  8. S. Celis and D. R. Musicant. Weka-parallel: machine learning in parallel. Technical report, Carleton College,CS TR, 2002.
  9. R. -E. Fan, K. -W. Chang, C. -J. Hsieh, X. -R. Wang and C. -J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning. Research, 9:1871–1874, 2008
  10. J. E. Gewehr, M. Szugat, and R. Zimmer. BioWeka — extending the weka framework for bioinformatics. Bioinformatics, 23(5):651–653, 2007.
  11. K. Hornik, A. Zeileis, T. Hothorn, and C. Buchta. RWeka: An R Interface to Weka, 2009. R package version 0. 3-16.
  12. S. Celis and D. R. Musicant. Weka-parallel: machine learning in parallel. Technical report, Carleton College,CS TR, 2002
Index Terms

Computer Science
Information Sciences

Keywords

K-means Clustering Euclidean Distance Spatial Data Mining Weka Interface