Outliers deviate from the norm. But outliers can tell us more about our data, how we gather it, and what is in it, if we examine the data set carefully.

Determining whether a data point is an "outlier" - a result that does not fit, is too high or too low, is extreme or discordant - is difficult when using small data sets, such as the data from three, ...

As data sets get bigger and the variety of features grows, it becomes harder to detect outliers. “Everything is trickier here, it’s hard to compute averages and look at what kinds of outliers you ...

Machine learning is particularly good at digesting large amounts of data very quickly and identifying patterns or finding anomalies or outliers in that data.

In a world of large data sets, techniques such as winsorisation can be used to handle outliers by keeping the observation but reducing its influence on aggregate metrics like value weighted ...

Location of Several Outliers in Multiple-Regression Data Using Elemental Sets Douglas M. Hawkins, Dan Bradu and Gordon V. Kass Technometrics , pp. 197-208 (12 pages) ...

Sports Data Labs, Inc. Announces Issuance of New U.S. Patent Covering its Novel Generative AI-Based Method for Creating Synthetic Data to Replace Missing and Outlier Data Values ...

Australian inflation data for the third quarter due on Wednesday are expected to show the country remains a global outlier, with economists widely expecting a benign report on consumer prices.