Dengue Disease Detection using K- Means, Hierarchical, Kohonen- SOM Clustering
P. Yogapriya1, P. Geetha2
1P. Yogapriya, Department of Computer Science, Alagappa University/ Dr. Umayal Ramanathan College for Women/ Karaikudi, India.
2Dr. P. Geetha, Department of Computer Science, Alagappa University/ Dr. Umayal Ramanathan College for Women/ Karaikudi, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 904-907 | Volume-8 Issue-10, August 2019 | Retrieval Number: J90660881019/2019©BEIESP | DOI: 10.35940/ijitee.J9066.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data Mining is the process of extracting useful information. Data Mining is about finding new information from pre-existing databases. It is the procedure of mining facts from data and deals with the kind of patterns that can be mined. Therefore, this proposed work is to detect and categorize the illness of people who are affected by Dengue through Data Mining techniques mainly as the Clustering method. Clustering is the method of finding related groups of data in a dataset and used to split the related data into a group of sub-classes. So, in this research work clustering method is used to categorize the age group of people those who are affected by mosquito-borne viral infection using K-Means and Hierarchical Clustering algorithm and Kohonen-SOM algorithm has been implemented in Tanagra tool. The scientists use the data mining algorithm for preventing and defending different diseases like Dengue disease. This paper helps to apply the algorithm for clustering of Dengue fever in Tanagra tool to detect the best results from those algorithms.
Keywords: Clustering, Data Mining, Dengue, Hierarchical, K-Means, Kohonen-SOM, Tanagra
Scope of the Article: Data Mining