Outlier Detection Techniques
published: Oct. 1, 2010, recorded: July 2010, views: 9535
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
This tutorial provides a comprehensive and comparative overview of a broad range of state-of-the-art algorithms for finding outliers in massive data sets. It sketches important applications of the introduced methods, and presents a taxonomy of existing approaches. In addition, relationships between the algorithmic approaches of each category of the taxonomy are discussed. Last but not least, at least one algorithm of each category is used for an empirical evaluation of the different approaches for outlier detection. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain where outlier detection methods are required.
Download slides: kdd2010_krogel_odt.ppt (3.9 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !
Reviews and comments:
This is the simplest, nonparametric outlier detection method in a one-dimensional feature space. Here outliers are calculated by means of the IQR (InterQuartile Range). The first and the third quartile (Q1, Q3) are calculated. An outlier is then a data point xi that lies outside the interquartile range.
Write your own review or comment: