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?

High-dimensional data can obscure patterns, making it harder 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 can enhance outlier detection effectiveness.

Are there specific industries where outlier detection is crucial?

Yes, industries like finance, healthcare, and cybersecurity rely heavily on outlier detection to identify fraud, anomalies, and threats.

Can you give an example of an outlier detection algorithm?

Sure! The Isolation Forest algorithm is popular for detecting outliers by isolating observations in the data.