Dengue Fever in Perspective of Clustering AlgorithmsKamran Shaukat1*, Nayyer Masood2, Ahmed Bin Shafaat1, Kamran Jabbar1, Hassan Shabbir1 and Shakir Shabbir1
- *Corresponding Author:
- Kamran Shaukat
University of the Punjab
Jhelum Campus, Pakistan
E-mail: [email protected]
Received Date: June 04, 2015 Accepted Date: August 12, 2015 Published Date: August 18, 2015
Citation: Shaukat K, Masood N, Shafaat AB, Jabbar K, Shabbir H, et al. (2015)Dengue Fever in Perspective of Clustering Algorithms. J Data Mining Genomics Proteomics 6:176. doi:10.4172/2153-0602.1000176
Copyright: © 2015 Shaukat K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Dengue fever is a disease which is transmitted and caused by Aedes Aegypti mosquitos. Dengue has become a serious health issue in all over the world especially in those countries who are situated in tropical or subtropical regions because rain is an important factor for growth and increase in the population of dengue transmitting mosquitos. For a long time, data mining algorithms have been used by the scientists for the diagnosis and prognosis of different diseases which includes dengue as well. This was a study to analyses the attack of dengue fever in different areas of district Jhelum, Pakistan in 2011. As per our knowledge, we are unaware of any kind of research study in the area of district Jhelum for diagnosis or analysis of dengue fever. According to our information, we are the first one researching and analyzing dengue fever in this specific area. Dataset was obtained from the office of Executive District Officer EDO (health) District Jhelum. We applied DBSCAN algorithm for the clustering of dengue fever. First we showed overall behavior of dengue in the district Jhelum. Then we explained dengue fever at tehsil level with the help of geographical pictures. After that we have elaborated comparison of different clustering algorithms with the help of graphs based on our dataset. Those algorithms include k-means, K-mediods, DBSCAN and OPTICS.