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Article:Data Clustering Method for Discovering Clusters in Spatial Cancer Databases

by Ritu Chauhan, Harleen Kaur, M.Afshar Alam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 6
Year of Publication: 2010
Authors: Ritu Chauhan, Harleen Kaur, M.Afshar Alam
10.5120/1487-2004

Ritu Chauhan, Harleen Kaur, M.Afshar Alam . Article:Data Clustering Method for Discovering Clusters in Spatial Cancer Databases. International Journal of Computer Applications. 10, 6 ( November 2010), 9-14. DOI=10.5120/1487-2004

@article{ 10.5120/1487-2004,
author = { Ritu Chauhan, Harleen Kaur, M.Afshar Alam },
title = { Article:Data Clustering Method for Discovering Clusters in Spatial Cancer Databases },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 10 },
number = { 6 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number6/1487-2004/ },
doi = { 10.5120/1487-2004 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:59:01.750364+05:30
%A Ritu Chauhan
%A Harleen Kaur
%A M.Afshar Alam
%T Article:Data Clustering Method for Discovering Clusters in Spatial Cancer Databases
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 6
%P 9-14
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The vast amount of hidden data in huge databases has created tremendous interests in the field of data mining. This paper discusses the data analytical tools and data mining techniques to analyze the medical data as well as spatial data. Spatial data mining includes discovery of interesting and useful patterns from spatial databases by grouping the objects into clusters. This study focuses on discrete and continuous spatial medical databases on which clustering techniques are applied and the efficient clusters were formed. The clusters of arbitrary shapes are formed if the data is continuous in nature. Furthermore, this application investigated data mining techniques such as classical clustering and hierarchical clustering on the spatial data set to generate the efficient clusters. The experimental results showed that there are certain facts that are evolved and can not be superficially retrieved from raw data.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Clustering K-means Hierarchical agglomerative clustering (HAC) SEER