Outlier detection, in its most basic sense, is the process of identifying data points, events or observations that deviate significantly from the rest of a dataset. It is commonly used in computer science and other related fields as a method of helping to identify unusual patterns or behaviors that may otherwise go undetected. Outlier detection is an integral part of data mining and machine learning due to its ability to help identify anomalies, errors or unknown data points.
Outlier detection utilizes a variety of methods, including statistical methods, such as clustering and distance-based methods, and distance-based methods. Statistical methods attempt to identify data points that are significantly far from the average point in a dataset. Clustering methods attempt to group similar points together, while distance-based methods identify points that are too far away from most of the other points in the dataset.
Outliers can be either positive or negative, meaning they can either be overestimated or underestimated values. Overestimation values are considered false positives, while underestimation are called false negatives. Outliers can be caused due to a variety of issues, such as incorrect data input, data corruption, human errors, or random sampling errors.
It is important to correctly identify outliers in datasets, since they can potentially have a large impact on results if they do not become found and accounted for. Having either false positives or false negatives can lead to producing wrong results or irregular patterns, which can lead to incorrect decisions. As such, outlier detection methods allow for identifying and dealing with outliers within datasets. Outlier detection is an effective and important step in the data preprocessing stage, as it aids in properly identifying possible issues with the dataset.
