UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 4
April-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIRER06054


Registration ID:
308576

Page Number

229-233

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Title

ANALYSING THE SPATIAL DATA OPTIMISATION WITH MACHINE LEARNING

Abstract

This study is undertaken to analysis and to compare between two algorithms- existing feature algorithm and machine learning algorithms with spatial data. Here Microsoft Visual Studio.Net is used as a tool for better visualization and accuracy of the spatial data. Spatial data is about instances located in a physical space.when spatial information becomes dominant interest, spatial data mining should be applied ,with the results of the two algorithm we will be declaring which algorithm is best when compared.

Key Words

clarans algorithm, proMISH algorithm.

Cite This Article

"ANALYSING THE SPATIAL DATA OPTIMISATION WITH MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 4, page no.229-233, April-2021, Available :http://www.jetir.org/papers/JETIRER06054.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"ANALYSING THE SPATIAL DATA OPTIMISATION WITH MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 4, page no. pp229-233, April-2021, Available at : http://www.jetir.org/papers/JETIRER06054.pdf

Publication Details

Published Paper ID: JETIRER06054
Registration ID: 308576
Published In: Volume 8 | Issue 4 | Year April-2021
DOI (Digital Object Identifier):
Page No: 229-233
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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