Outlier Detection and Trend Detection: Two Sides of the Same Coin

Outlier detection is commonly defined as the process of finding unusual, rare observations in a large data set, without prior knowledge of which objects to look for. Trend detection is the task of finding some unexpected change in some quantity, such as the occurrence of certain topics in a textual data stream. Many established outlier detection methods are designed to search for low-density objects in a static data set of vectors in Euclidean space. For trend detection, high volume events are of interest and the data set is constantly changing. These two problems appear to be very different at first. However, they also have obvious similarities. For example, trends and outliers likewise are supposed to be rare occurrences. In this paper, we discuss the close relationship of these tasks. We call to action to investigate this further, to carry over insights, ideas, and algorithms from one domain to the other.