| Type of Presentation |
Oral ( 0 ) / Poster ( ) / Anything ( ) |
| Scope and Interests |
Clustering Algorithm analysis |
| Title of Paper |
An Analysis of Diabetics Data Using k-Means Clustering Algorithm |
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Corresponding Author(s)
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Name:T.
Velmurugan1, A.Dharmarajan2 Affiliation:1Associate Professor, PG and Research Department of
Computer science, D. G. Vaishnav College, Chennai, India2Assistant Professor, Department of Computer
Science, Ayya Nadar Janaki Ammal College,Sivakasi, India
e-mail:velmurugan_dgvc@yahoo.co.in, mailtodharmarajan@gmail.com
Tel: |
Author(s)
name / Affiliation / e-mail |
1) Name:T. Velmurugan1, A.Dharmarajan2
Affiliation:1Associate Professor, PG and Research Department of Computer science, D. G. Vaishnav College, Chennai, India2Assistant Professor, Department of Computer Science, Ayya Nadar Janaki Ammal College,Sivakasi, India
e-mail:velmurugan_dgvc@yahoo.co.in, mailtodharmarajan@gmail.com
Tel:
2)
3) |
| Abstract |
In the medical
field, huge data is available, which leads to the need of a powerful data
analysis for the extraction of useful information. Several studies have been
carried out in the domain of data mining to improve the capability of data
analysis on large datasets. Clustering is the important
aspect of data mining which is used to analyze much kind of data. It is the process
of grouping of data, where the grouping is recognized by finding similarities
between data based on their features. There are number of techniques proposed
by several developers, they were analyzed the clustering algorithms in data
mining. In this research work, the large dataset of diabetics is
collected from the reputed hospitals among the specific city. The received
information can have the details of person who are affected by diabetics in
young age. The Diabetician suggested parameters are only used for the input
data in this analysis. For this k-Means algorithm is applied. To evaluate the
clustering quality, the distance between two data points are taken for analysis
and creation of proper clusters. The result of clustering quality, performance
and computer complexity is also analyzed. Highly affected persons are
identified through this clustering approach. Finally, the explicit result is
generated by using the outcome cluster. The resulted clusters have the details
of person who are affected diabetic in young age and specific reason. This type
of outcome is used to continue the treatment type or help for the physician or
diabetician.
Keywords: Diabetic data analysis, k-Means algorithm,
Performance Analysis, Clustering Method. |
| Keywords |
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