There are two areas in data analysis that look for anomalies: outlier detection and novelty detection.
A new object or novelty is an object that differs in its properties from the objects in the training dataset. Unlike an outlier, the new object is not in the dataset itself, but it can appear at any point after a system has started working. Its task is to detect when it appears. For example, if we were to analyze existing temperature measurements and identify abnormally high or low values, then we would be detecting outliers. On the other hand, if we were to create an algorithm that, for every new measurement, evaluates the temperature's similarity to past values and identifies significantly unusual ones, then we are detecting novelties.
The reasons for outliers appearing include data errors, the presence of noise, misclassified...
