A Comparative Analysis of Uncertainty Based Feature Selection Technique for Medical Data |
Author(s): |
| K.Anbumathi , Bharathiyar Arts and Science College for Women, Deviyakurichi, Salem; P.Sivaranjini, Bharathiyar Arts and Science College for Women, Deviyakurichi, Salem |
Keywords: |
| Common Language Runtime (CLR), Clustering Threshold (CT), Clustering Index (CI) |
Abstract |
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Stream clustering in healthcare industry can carry significant consequence by discovering disease patterns or by providing better clinical supports. Online stream clustering has several applications connected with it like news filtering, ad filtering, and theme recognition. However, clustering particularly for health care industry has not come into consideration yet. In addition, existing clustering methods rarely consider the variety of continuous data and may lead to unsatisfactory results. As a result, implementing existing stream clustering for healthcare industry may not be sustainable for the long run. Motivated from the problem, we propose a clustering algorithm for sensory data in healthcare organization based on dynamic feature selection known as PCEHRClust. Using a qualitative analysis we show that PCEHRClust is a suitable algorithm for health care industry. |
Other Details |
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Paper ID: IJSRDV4I110248 Published in: Volume : 4, Issue : 11 Publication Date: 01/02/2017 Page(s): 373-375 |
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