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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
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Index Terms

Computer Science
Information Sciences

Keywords

K-means Clustering Euclidean Distance Spatial Data Mining Weka Interface