Machine learning, especially unsupervised learning, can offer powerful and flexible solutions for anomaly detection, by using clustering algorithms to group similar data points and detect outliers.

For example, you can cluster news articles by genre, customer reviews by sentiment, or tweets by topic. For effective clustering and anomaly detection ... is a technique in unsupervised learning ...

Industries from retail to finance are using clustering to personalize services, detect fraud, monitor equipment and improve ...

This tutorial aims to instruct the attendees to the principles, application and evaluation of anomaly-based techniques for intrusion detection, with a focus on unsupervised algorithms, which are able ...

We then employ clustering algorithms to identify clusters containing normal data, which are then used to train unsupervised anomaly detection models ... an online learning mechanism updates the model ...

Clustering algorithms are a form of unsupervised learning algorithm ... Clustering enables anomaly detection in manufacturing, helping to spot defective parts. And in the life sciences, clustering ...

Anomaly detection ... in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role ...

To solve these problems, based on the knowledge distillation framework, this paper proposes an unsupervised anomaly detection algorithm—Bidirectional knowledge distillation AD (BKD). This algorithm ...