| dc.description.abstract | Outlier detection is studied and applied in many domains. Outliers arise due to different
reasons such as fraudulent activities, structural defects, and mechanical issues. The
detection of outliers is a challenging task that can reveal system faults, fraud, and save
people’s lives. Outlier detection techniques are often domain-specific. The main
challenge in outlier detection relates to modelling the normal behaviour in order to
identify abnormalities. The choice of model is important, i.e., an unsuitable data model
can lead to poor results. This requires a good understanding and interpretation of the data,
the constraints, and requirements of the domain problem. Outlier detection is largely an
unsupervised problem due to unavailability of labelled data and the fact that labelled data
is expensive. During the extraction process, the outsource may damage their original data
set and that will be defined as the intrusion. To avoid the intrusion and maintain the
anomaly detection in a high densely populated environment is another difficult task. For
that purpose, Grid Partitioning for Outlier Detection (GPOD) has been proposed for high
density environment. This technique will detect the outlier using the grid partitioning
approach and density based outlier detection scheme. Initially, all the data sets will be
split in the grid format. Allocate the equal amount of data points to each grid. Compare
the density of each grid to their neighbor grid in a zigzag manner. Based on the response,
lesser density grid will be detected as outlier function as well as that grid will be
eliminated. This proposed Grid Partitioning for Outlier Detection (GPOD) has reduced
the complexity and increases the accuracy. | en_US |
