What is it about?
Data analysis is an important challenge for best decisional system based on data. We have shown for the first time that faulty data can defect the decision. This was previously thought impossible as abnormal data is very strongly important to find it. We have used synthetic outliers in real data bases in experiments to confirm the utility of density based clustering approach .
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Why is it important?
We define rules for determining which are normal, abnormal and faulty data used density techniques for data clustering
Perspectives
Writing this article was a great pleasure as it has focus on data based outlier detection with whom it will have long standing in future research ex. IoT, smart systems. This article also lead to rare disease groups contacting me and ultimately to a greater involvement in data analysis research.
Mr Aymen ABID
National School of Engineering of Sfax
Read the Original
This page is a summary of: Outlier detection for Wireless Sensor Networks using density based clustering approach , IET Wireless Sensor Systems, February 2017, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-wss.2016.0044.
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Contributors
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