Abstract: A computer-implemented method and system for detecting anomalies in an unlabeled data set of data records is provided. The method applies a classification method to a training data set obtained from the unlabeled data set to generate a classification model associating a predicted output class to each training data record. The predicted output class is compared with the original output class, in order to detect an anomalous data record in the presence of a discrepancy between the original and the predicted output class. The method may assign a confidence score to the predicted output class by the classification model, wherein the anomalous data record is detected on the basis of a threshold of the confidence score. For example, the classification method may be based on Boolean functions synthesis by a Shadow Clustering algorithm, and the classification model is in the form of a set of conditional rules.