The week's pick
Reseach Article
Integrating Spatial Data Mining Technique to Identify Potential Landsat Data using K-Means and BPNN Algorithm
by
N.Naga Saranya,
M.Hemalatha
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 30 - Number 9 |
| Year of Publication: 2011 |
| Authors: N.Naga Saranya, M.Hemalatha |
10.5120/3665-5126
|
N.Naga Saranya, M.Hemalatha . Integrating Spatial Data Mining Technique to Identify Potential Landsat Data using K-Means and BPNN Algorithm. International Journal of Computer Applications. 30, 9 ( September 2011), 16-21. DOI=10.5120/3665-5126
@article{
10.5120/3665-5126,
author = {
N.Naga Saranya,
M.Hemalatha
},
title = { Integrating Spatial Data Mining Technique to Identify Potential Landsat Data using K-Means and BPNN Algorithm },
journal = {
International Journal of Computer Applications
},
issue_date = { September 2011 },
volume = { 30 },
number = { 9 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = {
16-21
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume30/number9/3665-5126/
},
doi = { 10.5120/3665-5126 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:38.190201+05:30
%A N.Naga Saranya
%A M.Hemalatha
%T Integrating Spatial Data Mining Technique to Identify Potential Landsat Data using K-Means and BPNN Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 30
%N 9
%P 16-21
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Spatial Data mining is one of the challenging field in data mining. The explosive development of spatial data and common use of spatial databases highlight the need for the automated detection of spatial knowledge. Computing data mining algorithms such as clustering on massive spatial data sets is still not feasible nor efficient today. In this research first we elaborate a study on data clustering, particularly on spatial data clustering. Here we introduce a k-means algorithm that is based on the data stream paradigm. Some of the existing classical clustering algorithm and the proposed BPNN were tested with UCI repository datasets for spatial data clustering and classification. Several tests were made on the system and overall significant results were achieved. Proposed method is an influential tool for the classification of multidimensional spatial data sets.
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Index Terms
Computer Science
Information Sciences
Keywords