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Research Paper | Computer Science & Engineering | India | Volume 5 Issue 7, July 2016
Cluster in High Dimensional Data to Detect Outlier
Sonali. A. Patil | Snehal. S. Thokale
Abstract: In computer world data should be secured. To find deviated data or fraud in data some techniques are introduced and that method is nothing but Outlier Detection. , it helps to improve find out frauds and intruders. Outlier detection techniques are used for batch system also but for huge data we have to more careful to find such type of data. So, to overcome this over sampling principle component analysis is used. By using Principal Component Analysis (PCA), it is helpful to find Outlier. In this system our aim to detect the presence of Outlier from a large amount of data using an online updating method. As we are using Oversampling Principal Component analysis (osPCA), there is no need to store data matrix or covariance matrix each time and thus approach is to find anomalies in online data stream or large amount of data problems. In our proposed system we capture only UDP packets will include. Along with this we are proposing algorithm for clustering.
Keywords: Detection of Outlier, online updating, oversampling, principal components analysis, TCP And UDP packets, DBSCAN cluster algorithm
Edition: Volume 5 Issue 7, July 2016,
Pages: 144 - 147
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Review Papers, Computer Science & Engineering, India, Volume 3 Issue 12, December 2014
Pages: 687 - 690Anomaly Detection of Online Data using Oversampling Principal Component Analysis
Supriya A. Bagane | J. L. Chaudhari [3]
Downloads: 150
Research Paper, Computer Science & Engineering, India, Volume 3 Issue 10, October 2014
Pages: 11 - 18Anomaly Detection via Online Over-Sampling Principal Component Analysis
Pachunoori Naresh | Garine Bindu Madhavi [2]