Different automatic tasks are facilitated via outlier detection in datasets using a Weighted Histogram-based Outlier Scoring (W-HBOS). An initial set of features is extracted from a processed dataset. The initial set of features is further filtered by applying robust statistics for size reduction. A second round of automatic feature selection is implemented based on maximum-entropy estimation so that a selected set of features that can give maximum possible information from different dimensions towards detecting anomalies are selected. The selected set of features are transformed to generate principal components that are provided to the W-HBOS-based model for outlier detection. A subset of outliers in one of the directions can be selected and reason codes are identified using back transformation for the execution of a desired automatic task.