Join 4,000+ technologists, decision makers and community members in Raleigh, NC

October 13 - 15


Machine Learning

K-Means for Anomaly Detection

Anais Dotis-Georgiou   

Everyone seems to be eager to incorporate machine learning into their anomaly detection solutions. However, does it always make sense to use machine learning? Using statistical methods to detect one off-peaks in time series data is extremely effective and efficient. However, statistical methods fail when trying to detect contextual or collective anomalies. If you want to alert on these type anomalies, you probably need to use some type of machine learning. In this talk, I will share three statistical methods, introduce two popular machine learning algorithms, and describe the relationship between contextual and collective anomalies. Finally, I will show you how easy it is to use K-Means to alert on a contextual anomaly in EKG data.