Detecting anomalous records in categorical datasets
Kaustav Das, Jeff Schneider · 2007
We consider the problem of detecting anomalies in high aritycategorical datasets. In most applications, anomalies are defined as datapoints that are "abnormal". Quite often we have access to data which consists mostly of normal records, a long with a small percentage of unlabelled anomalous records. We are interested in the problem of unsupervised anomaly detection, where we use the unlabelled data for training, and detect records that do not follow the definition of normality.