Cell-DROS: A Fast Outlier Detection Method for Big Datasets

Outlier detection is one of the obstacles of big dataset analysis because of its time consumption issues. This paper proposes a fast outlier detection method for big datasets, which is a combination of cell-based algorithms and a ranking-based algorithm with various depths. A cell-based algorithm is proposed to transform a very large dataset to a fairly small set of weighted cells based on predefined lower and upper bounds. A ranking-based algorithm with various depths is modified and applied on weighted cells to calculate outlier scores and sort cells based on their outlier scores. Finally, an outlier obtaining algorithm is proposed to identify ids of outliers from the ranked cells and eliminate outliers from the given datasets. Experiment results show that the proposed method can produce the same results when compared to the previous rank-difference outlier detection algorithm but it can reduce up to 99% of executing time.