Grid Partitioning For Anomaly Detection (Gpad) In High Density Distributed Environment For Mining Techniques

Abstract

Anomaly detection is the most important task in data mining techniques. This helps to increase the scalability, accuracy and efficiency. 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 Anomaly Detection (GPAD) 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 Anomaly Detection (GPAD) has reduced the complexity and increases the accuracy and these will be proven in simulation part.