Classification Rules Using Decision Tree for Dengue Disease
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 3
Abstract
Spatial data mining becomes more interesting and important as more spatial data have been accumulated in spatial databases. Spatial patterns are of great importance in many GIS applications that yield equal to association rules of a business i.e. On Line Transaction Processing (OLTP). Mining the spatial co-location patterns is an important spatial data mining task with broad applications. Certain operations that incorporate methods of analyses and summarization are very much crucial for organizations having large data sets of spatial data. . Decision tree is one of the learning algorithms which possess certain advantages which make it suitable for discovering rules for data mining application. The decision tree has been applied to classify the population in an area based on the chances of hitting a disease. This paper intended to discover the rules for the disease hit using decision tree algorithm. The paper also explores what rule can act in this area for the future prediction. The objective is to creating a prediction model, using decision tree for predicting the chances of occurrences of dengue diseases in a tribal area..
Authors and Affiliations
N K Kameswara Rao, Dr. G P Saradhi Varma, Dr. M. Nagabhushana Rao
Keywords
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