Anomaly detection is a technique used in data analysis to identify unusual patterns or outliers that differ significantly from the norm. This process is essential in various fields, such as finance, healthcare, and cybersecurity, where detecting irregularities can prevent fraud, diagnose diseases, or identify security breaches. The methods for anomaly detection can be statistical, machine learning-based, or rule-based. For example, algorithms like k-means clustering or support vector machines can help classify data points and highlight those that deviate from expected behavior, enabling organizations to take timely action.