a clustering method for seismic zone identification and spatial data mining
Soriful Hoque1,,Salim Istyaq,Mohammad Mushir Riaz
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:1 Issue:2 Year: 12 September,2013 Pages:66-88
Abstract
This paper shows how it made possible in geographical science to observe the seismic zone, clustering of highly sensitive earthquake zone and spatial data clustering during important geographical processes. This paper shows simple density based and K- Mean clustering technique. Density-Based clustering is done here using density estimation and by searching regions which are denser than a given threshold and to form clusters from these dense regions by using connectivity and density functions. Also we defined some optimal no of K locations for K-Mean clustering where the sum of the distance from every point to each of the K centers is minimized what is called global optimization. With this dataset it forms clusters using density estimation and K-Mean clustering. Also it correlates the clustering pattern by applying co-relation algorithm and proximity measure algorithm; hence it easily removes noisy data. This scheme can extract clusters efficiently with reduced number of comparisons.
Kewords
Clustering, co-relation, density based, K-Mean, proximity measure, spatial dataset, seismic zone
Reference
[1] Allard D. and Fraley C.:”Non Parametric Maximum Likelihood Estimation of Features in Spatial Point Process Using Voronoi Tessellation”, Journal of the American Statistical Association, to appear in December 1997.[Available at http://www.stat.washington.edu/tech.reports/tr293R.ps]. [2] Beckmann N., Kriegel H.-P., Schneider R., Seeger B.: ‘TheR*- tree: An Efficient and Robust Access Method for Points and Rectangles’, Proc. ACM SIGMOD Int. Conf. on Management of Data, Atlantic City, NJ, 1990, pp. 322-331. [3] Banfield J. D. and Raftery A. E.: “Model based Gaussian and non-Gaussian clustering”, Biometrics 49, September 1993, pp. 803-821. [4] Byers S. and Raftery A. E.: “Nearest Neighbor Clutter Removal for Estimating Features in Spatial Point Processes”, Technical Report No. 305, Department of Statistics, University of Washington. [Available at http://www.stat.washington.edu/ tech.reports/tr295.ps] [5] Devore J. L.: ‘Probability and Statistics for Engineering and the Sciences’, Duxbury Press, 1991. [6] Ester M., Kriegel H.-P., Sander J., Xu X.: “A Density- Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, Proc. 2cnd Int. Conf. on Knowledge Discovery and Data Mining, Portland, Oregon, 1996, AAAI Press, 1996. [7] Ester M., Kriegel H.-P., Xu X.: “Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification”, Proc. 4th Int. Symp. On Large Spatial Databases, Portland, ME, 1995, in: Lecture Notes in Computer Science, Vol. 951, Springer, 1995, pp.67-82. [8] Fayyad U. M.,.J., Piatetsky-Shapiro G., Smyth P.: “From Data Mining to Knowledge Discovery: An Overview”, in: Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, 1996, pp. 1 - 34. [9] Gueting R. H.: “An Introduction to Spatial Database Systems”, in: The VLDB Journal, Vol. 3, No. 4, October 1994, pp.357-399. [10] Kaufman L., Rousseeuw P. J.: “Finding Groups in Data: An Introduction to Cluster Analysis”, John Wiley & Sons,1990. [11] McKenzie M., Miller R., and Uhrhammer R.: “Bulletin of the Seismographic Stations”, University of California, Berkeley. Vol. 53, No. 1-2. [12] Muise R. and Smith C.: “Nonparametric minefield detection and localization”, Technical Report CSS-TM- 591-91, Naval Surface Warfare Center, Coastal Systems Station. [13] Ng R. T., Han J.: “Efficient and Effective Clustering Methods for Spatial Data Mining”, Proc. 20th Int. Conf. on Very Large Data Bases, Santiago, Chile, 1994, pp. 144-155.
