Here's a breakdown of some commonly used anomaly detection methods, along with their strengths, weaknesses, and typical use cases:

1. Density-Based Methods

• Key Example: Local Outlier Factor (LOF)
• Input: Dataset of numerical features.
• Output: Anomaly score for each data point, higher scores indicate greater likelihood of being an outlier.
• Strengths:
  • Can detect anomalies of varying densities.
  • Not overly sensitive to global data distribution.
• Weaknesses:
  • Computationally expensive, especially for large datasets.
  • Sensitive to the choice of distance metric.
• Use Case: Identifying fraudulent credit card transactions where spending patterns significantly deviate from a user's usual behavior.

2. Isolation Forests

• Input: Dataset of numerical or categorical features.
• Output: Anomaly score for each data point, with higher scores corresponding to easier isolation.
• Strengths
  • Efficient for large datasets.
  • Intuitive concept of isolating outliers.
  • Relatively few hyperparameters to tune.
• Weaknesses:
  • Struggles if 'normal' data has a wide range of diverse patterns.
  • Sensitive to the presence of irrelevant features (noise).
• Use Case: Detecting unusual network traffic patterns that might indicate potential intrusions.

3. Clustering-Based Methods

• Key Example: K-means clustering + distance
• Input: Dataset of numerical features.
• Output: Cluster assignments for data points, anomaly scores based on distance from cluster centers.
• Strengths:
  • Well-suited for finding cluster-based outliers.
  • Versatile with different clustering algorithms.
• Weaknesses:
  • Assumes anomalies form distinct clusters from the main data.
  • Performance depends on the quality of the clustering results.