M.TECH COMPUTER SCIENCE

Outlier detection is a fundamental issue in data mining, specifically it has been used to detect and remove anomalous objects from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, network intrusions or human errors. Firstly, this thesis presents a theoretical overview of outlier detection approaches. A novel outlier detection method is proposed and analyzed, it is called Clustering Outlier Removal (COR) algorithm. It provides efficient outlier detection a

Data mining gives good monitoring environment in data sharing applications. In different number of data sharing applications discuss about noisy objects and without noisy objects. In previous system data leakages with fake objects, those objects are created here with fake agents. These situations are discussed about using some random operations specification process. Random fake objects show the results like as a data loss problems. Now we are introduces the new techniques like guilt aversion ag