What are common mistakes in anomaly detection?

What are some common mistakes people make in anomaly detection?

Some common mistakes include not preprocessing data properly, ignoring context, and using inappropriate algorithms.

Can you explain why preprocessing is so important?

Preprocessing helps to clean and normalize data, which is crucial for accurate anomaly detection.

What algorithms are often misused in this context?

Algorithms like k-means clustering can be misused if the data isn't well-distributed or if outliers are present.

How can I avoid these mistakes in my projects?

To avoid mistakes, ensure thorough data exploration, choose the right algorithms, and validate your results with domain knowledge.