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