Subspace Partitioning through Data Decomposition for Outlier Detection - Duari, Gouranga; Kumar, Rajeev (INFOCOMP Journal of Computer Science, 2024)
Decomposition for complexity minimization has long been a challenging approach. This paper presents a data decomposition approach as a pre-processor for outlier detection. The decomposition of the data using subspace partitioning makes homogeneous sub-groups. Consequently, it reduces the complexity of data patterns by isolating possible outliers into the sub-groups of monolithic character. This approach creates sub-groups of homogeneous data points based on the fitness of purpose. They optimize the outlier patterns in the sub-groups for subsequent mapping of outlier detectors onto the sub-groups. This decomposition strategy is found to be effective in reducing the complexity of learning for the detectors without deterioration in the overall detection rate. We experimented with this approach using different benchmark detectors on eight benchmark data sets. Our data decomposition approach is superior for identifying localized patterns in the partitions and offers a better generalization.