Diagnosis for Dengue Fever Using Spatial Data Mining

International Journal of Computer Trends and Technology (IJCTT)          
© - August Issue 2013 by IJCTT Journal
Volume-4 Issue-8                           
Year of Publication : 2013
Authors :N.Subitha, Dr.A.Padmapriya


N.Subitha, Dr.A.Padmapriya "Diagnosis for Dengue Fever Using Spatial Data Mining"International Journal of Computer Trends and Technology (IJCTT),V4(8):2646-2651 August Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract:- The research of spatial data is in its infancy stage and there is a need for an accurate method for rule mining. Association rule mining searches for interesting relationships among items in a given data set. This enable to extract pattern from spatial database using k-means algorithm which refers to patterns not explicitly stored in spatial databases. Since spatial Association mining needs to evaluate multiple spatial relationships among a large number of spatial objects. An interesting mining optimization method called progressive refinement can be adopted in spatial association analysis. The method first mines large data sets roughly using a fast algorithm and then improves the quality of mining in a pruned data set. The k-means algorithm randomly selects k number of objects, each of which initially represents a cluster mean or center. For each of the remaining objects, an object is assigned to the cluster to which it is most similar, based on the distance between the object and the cluster mean. Then it computes new mean for each cluster. This process iterates until the criterion function converges. The above concept is applied in the area of image segmentation where to apply the microscopic blood image as input and signals are filtered with the help of neural network to predict the best result about dengue fever.



[1] Dariusz Malyszkoa, Slawomir T. Wierzchonb “Standard and Genetic k-means Clustering Techniques in Image Segmentation” Technical University of Bialystok, Wiejska 45A, 15-351 Bialystok
[2] Ester M., Kriegel H.-P., and Sander J. 1997 “Spatial Data Mining: A Database Approach”, Proc. 5th Int. Symp. on Large Spatial Databases, Berlin, Germany, pp. 47-66.
[3] U. M. Fayyades, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds). Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
[4] W. Lu, J. Han, and B. C. Obi. Discovery of General Knowledge in Large Spatial Databases. In Proc. Far East Workshop on Geographic Information Systems pp. 275-289, Singapore, June 1993.
[5] 1. Jiawei Han and Micheline Kamber (2006), Data Mining Concepts and Techniques, published by Morgan Kauffman,
[6] Lillesand and Kiefer, 1994; Eastman, 1995”Image Classification
[7] S. B .Patil and Y . S. Kumaraswamy. “ Intelligent and Effective dengue blood samples Prediction System Using Data Mining and Artificial Neural Network”, European Journal of Scientific Research, 2009, ISSN 1450-216X Vol.31 No.4. pp.642-656 .
[4] Ramaswamy Palaniappan,” Biological Signal Analysis”, Ramaswamy Palaniappan and Ventus Publishing Aps, U.K, 2010
[8] Shekhar, S., and Chawla, S. 2003. Spatial Databases A Tour. Prentic e Hall (ISBN 0-7484-0064-6)
[9] P. Venkatesan* and S. Anitha. Application of a radial basis function neural network for diagnosis of diabetes mellitus. Current science, vol. 91, no. 9, 10 November, 2006, pp. 1195-1199.
[10] F. Yaghouby, A. Ayatollahi and R. Soleimani, Classification ofCardiac Abnormalities Using Reduced Features of Heart Rate Variability Signal”. World Applied Sciences Journal 6 (11), 2009, pp. 1547-1554.

Keywords : — Data mining, Spatial data mining, Spatial database, K-mean, Spatial relationship, Dengue fever.