LAUSR

Outlier detection approaches show their efficacy while extracting unforeseen knowledge in domains such as intrusion detection, e-commerce, and fraudulent transactions. A prominent method like the K-Nearest Neighbor (KNN)-based outlier detection (KNNOD) technique relies on distance measures to extract the anomalies from the dataset. However, KNNOD is ill-equipped to deal with dynamic data environment efficiently due to its quadratic time complexity and sensitivity to changes in the dataset. As a result, any form of redundant computation due to frequent updates may lead to inefficiency while detecting outliers. In order to address these challenges, we propose an approximate adaptive grid-based outlier detection technique by finding point density using kernel density estimate (KAGO) instead of any distance measure. The proposed technique prunes the inlier grids and filters the candidate grids with local outliers upon a new point insertion. The grids containing potential outliers are aggregated to converge on to at most top-N global outliers incrementally. Experimental evaluation showed that KAGO outperformed KNNOD by more than an order of $$\approx$$ 3.9 across large relevant datasets at about half the memory consumption.