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Journal of Engineering and Applied Sciences
Year: 2017 | Volume: 12 | Issue: 12 | Page No.: 3213-3219
DOI: 10.36478/jeasci.2017.3213.3219  
Analysis of Advanced Data Mining Prototypes in Spatial Data Analysis
M. Gangappa , C. Kiran Mai and P. Sammulal
 
Abstract: Retrieving useful knowledge from a large amount of geographical location with spatial data is a very interesting attribute as a high amount of geographical location with spatial data is retrieved from various running applications ranging from remote sensing to GIS (Geographical Information Systems) based environmental assessment and planning applications. Recently number of data mining techniques/methods has emerged to define the scope of data mining with relational and transactional databases. Especially, geographical location with spatial data mining analysis deals with retrieving implicit data related to geographical location with spatial data relations wherein patterns do not explicitly store geographical location with spatial databases from classifiable geographical location with spatial data analysis with geographical location with spatial attributes. In this study, we seek to analyze different geographical location with spatial data mining techniques. And also provide an overview of the common knowledge discovery data mining methodologies in geographical location with spatial data mining. Further, we discuss attribute classification and clustering for multi-three dimensional data. Finally, we provide a comparative analysis of all the overviewed ways followed in reliable geographical location with spatial data mining procedures.
 
How to cite this article:
M. Gangappa, C. Kiran Mai and P. Sammulal, 2017. Analysis of Advanced Data Mining Prototypes in Spatial Data Analysis. Journal of Engineering and Applied Sciences, 12: 3213-3219.
DOI: 10.36478/jeasci.2017.3213.3219
URL: http://medwelljournals.com/abstract/?doi=jeasci.2017.3213.3219