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Enhancement of Effective Spatial Data Analysis using R
Background: The availability of Spatial Data which is a part of GIS is growing day by day in an exponential manner. This high availability of data is throwing challenges to the research community to analyze and draw effective conclusions. The Present Study aims at requirement for effective analysis and to draw Conclusions. Methods/Statistical Analysis: Spatial Analysis requires logical relationships between attribute data and map features.Spatial data Analysis is not a simple single task it requires complex procedures in which combinational techniques namely Hybrid techniques are required for effective analysis. Mathematics and statistics are the fundamentals to spatial data analytics. In this paper, a realistic Spatial crime data set was considered for analysis. It involves different types of data mining Techniques like Clustering, Classification and Association rule mining techniques apart from Hybrid techniques. These hybrid Data mining Techniques were applied using R. Findings: The Hybrid Data Mining techniques with K-means Clustering and J48 Decision Tree Algorithm was developed and Applied for the enhancement of accuracy. Association Rule generation Apriori algorithm was applied on the resultant K-means clustered data set. The application of 3D visualization techniques also made for further analysis. Applications/Improvements: It is essentially required to analyze these complex spatial data sets effectively. So there is a need of hybrid Spatial Data Mining Techniques requirement for effective analysis and to draw Conclusions.
Complex Spatial Data Sets, Effective Spatial Data Analysis, Hybrid Data Mining Techniques, Spatial Data Analysis, 3D Visualization Techniques.
- Spatial analysis. Available from: https://en.wikipedia.org/ wiki/Spatial_analysis. Date accessed: 05/10/2015
- Spatial Data Analysis. Available from: http://dusk.geo.orst.edu/gis/Chapter14_notes.pdf. Date accessed: 05/10/2015
- Hemalatha M, Naga Saranya N. A Recent Survey on Knowledge Discovery in Spatial Data Mining. IJCI International Journal of Computer Science. 2011 May; 8(3):1–7.
- Rao T, Rajasekhar N, Rajinikanth TV. An efficient approach for Weather forecasting using Support Vector Machines. 2012 International Conference on Intelligent Network and Computing. 2012; 47. p. 208–12.
- RajiniKanth TV, Anuradha K, Premchand P, Murali Krishna IV. Weather Data Analysis of Rajasthan State using Data Mining Techniques. Journal of Advanced. 2011Apr; 3(2):82–6.
- Data mining. Available from: https://en.wikipedia.org/ wiki/Data_mining. Date accessed: 07/10/2015
- Spatial Data base. Available from: https://en.wikipedia.org/ wiki/Spatial_database. Date accessed: 07/10/2015
- Introduction to Spatial Databases. Available from: http:// www.spatial.cs.umn.edu/Book/sdb-chap1.pdf. Date accessed: 07/10/2015.
- Rajanikanth J, Rajinikanth TV, Prasad TVKP, Radha Krishna B. Analysis on Spatial Data Clustering Methods A Case Study. 2012 Oct- Dec; 3(4):51–4.
- Fayyad UMJ, Piatetsky-Shapiro G, Smyth P. From Data Mining to Knowledge Discovery: An Overview. Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park. 1996; 1–34.
- Shekhar S, Zhang P, Huang Y, Vatsavai R. Trend in Spatail Data Mining, as a chapter to appear in Data Mining: Next Generation Challenges and Future Directions. 2013 AprJun; 4(2):1–6.
- Vijay Kumar A, RajiniKanth TV. Estimation of the Influence of Fertilizer Nutrients Consumption on the Wheat Crop yield in India- a Data mining Approach. 2013 Dec; 3(2):316–20.
- Vijay Kumar A, RajiniKanth TV. A Data Mining Approach for the Estimation of Climate Change on the Jowar Crop Yield in India. 2013 Dec; 2(2):16–20.
- Vijay Kumar A, RajiniKanth TV. Estimation of the Influential Factors of rice yield in India. 2nd International Conference on Advanced Computing methodologies. 2013 Aug; 459–65.
- Gueting RH. An Introduction to Spatial Database Systems, Special Issue on Spatial Database Systems of the VLDB Journal. 1994 Oct; 3(4):357–99.
- Koperski K, Adhikary J, Han J. Knowledge Discovery in Spatial Databases: Progress and Challenges. Proceedings of SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Technical Report 96-08, University of British Columbia, Vancouver, Canada, 1996.
- Zhang P, Steinbach M, Kumar V, Shekhar S, Tan P, Klooster S, Potter C. Discovery of Patterns of Earth Science Data using Data Mining, as a chapter to appear in Next Generation of Data Mining Applications, IEEE Press. 2004; 167–87.
- Parimala M, Lopez D, Kaspar S. K-Neighbourhood Structural Similarity Approach for Spatial Clustering. Indian Journal of Science and Technology. 2015 Sep; 8(23):1–11.
- Rajinikanth TV, Balaram VVSSS, Rajasekhar N. Analysis of Indian temperature data using Data mining Techniques. International Conference Advances in Computing and Information Technology. 2014 May. p. 89–94.
- Spatial Key Support. Available from: https://support.spatialkey.com/. Date accessed: 09/10/2015.
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