Application of Advanced Clustering Methods in Geographic Data Analysis and Mining Pradeep Mohan Student, Dept. of Computer Science and Engg Birla Institute of Technology, Mesra, India Email: mohan.pradeep@gmail.com Sivakumar R Associate Prof. Dept. of Remote Sensing Birla Institute of Technology, Mesra, India Email: skm_ram@yahoo.com Anish Mitra Dept. of Electrical and Electronics Engg Birla Institute of Technology, Mesra, India Email: mitra.anish@gmail.com Clustering is a statistical technique useful in finding interesting structures and clusters in any given dataset and is one of the most important methods in Geographic Data analysis or Geographic Data Mining. Many clustering algorithms have been studied in the literature for geographic data. This paper describes the use of advanced data clustering like fuzzy – c-means clustering, Genetic Algorithms and R*Tree. Also describes the use of clustering methods to evolve an action plan for a study area under consideration. Here, fuzzy- c-means clustering and Genetic Algorithms have been applied to non-spatial data consisting of lat-long positions of points. The above algorithms were input with non-spatial data of points with lat-long and nine other spatial parameters. The output was the clusters and the cluster centers of the whole data set. The same set of points are then clustered using R*Tree clustering to demonstrate spatial data clustering. The final orientation of the points in the R*Tree are the output clusters where in each parent rectangle represents a cluster. The effect of the above clustering methods before and after a data mining process viz. Association Rule mining has been studied and illustrated.