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Techniques for Machine Learning based Spatial Data Analysis: Research Directions

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
M. Gangappa, C. Kiran Mai, P. Sammulal
10.5120/ijca2017914643

M Gangappa, Kiran C Mai and P Sammulal. Techniques for Machine Learning based Spatial Data Analysis: Research Directions. International Journal of Computer Applications 170(1):9-13, July 2017. BibTeX

@article{10.5120/ijca2017914643,
	author = {M. Gangappa and C. Kiran Mai and P. Sammulal},
	title = {Techniques for Machine Learning based Spatial Data Analysis: Research Directions},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {1},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {9-13},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume170/number1/28032-2017914643},
	doi = {10.5120/ijca2017914643},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Today, machine learning techniques play a significant role in data analysis, predictive modeling and visualization. The main aim of the machine learning algorithms is that they first learn from the empirical data and can be utilized in cases for which the modeled phenomenon is hidden or not yet described. Several significant techniques like - Artificial Neural Network, Support Vector Regression, k-Nearest Neighbour, Bayesian Classifiers and Decision Trees are developed in past years to acheive this task. The first and most complicated problem concerns is the amount of available data. This paper reviews state-of-the-art in the domain of spatial data analysis by employing machine learning approaches. First various methods have been summarized, which exist in the literature. Further, the current research scenarios which are going on in this area are described. Based on the research done in past years, identification of the problem in the existing system is also presented in this paper and have given future research directions.

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Keywords

Spatial data Analysis, ANN, SVM, Feature Extraction, Classification, Rough sets