What are the main challenges in outlier detection?

What are the main challenges in outlier detection?

The main challenges include defining what constitutes an outlier, dealing with high-dimensional data, and ensuring robustness against noise.

How does dimensionality affect outlier detection?

In high-dimensional spaces, data points become sparse, making it difficult to identify outliers due to the curse of dimensionality.

What techniques can be used to improve outlier detection?

Techniques like clustering, statistical tests, and machine learning algorithms such as Isolation Forest can enhance detection.

Are there specific industries that face more challenges with outlier detection?

Yes, industries like finance, healthcare, and cybersecurity often face unique challenges due to the nature of their data.

Can you give examples of real-world outlier detection applications?

Sure! Applications include fraud detection in banking, anomaly detection in network security, and identifying rare diseases in healthcare.