A CONTEXTUAL OUTLIER DETECTION ALGORITHM BASED ON WEIGHTED PROBABILITY DENSITY

A contextual outlier data detection algorithm is proposed by using weighted probability density. In the algorithm, the Gaussian mixture model and the sparsity matrix were used to determine the correlation subspace. The weighted probability density local anomaly factor formula was used to calculate the outlier factor of the data object in the relevant subspace, which could effectively reflect and describe the degree of inconsistency between data objects and their surrounding data objects. N data objects with the largest outlier factor value were selected as outliers, and the value of outlier factor, correlation subspace attributes and local datasets were taken as their contextual information, effectively improving the interpretability and understandability of outlier data objects. Experimental results validate the effectiveness of this algorithm by using artificial dataset and UCI datasets.