Anomaly detection techniques use statistical methods or data mining to identify outliers in data. Distance-based techniques identify outliers based on their distance from other data points, while density-based techniques find outliers in low-density areas compared to normal data points in high-density areas. Another method is local outlier factor, which can detect outliers in varying density regions by measuring how isolated objects are from their neighbors.

1. 11.Anomaly Detection

2. ANOMALY DETECTION
1. Causes of Outliers
i. Data errors:
ii. Normal variance in the data:
iii. Data from other distribution classes:
iv. Distributional assumptions:

3. Anomaly Detection Techniques
1. Outlier Detection Using Statistical Methods
1. Outlier Detection Using Data Mining
i. Distance based:
ii. Density based:
iii. Distribution based:
iv. Clustering:
v. Classification techniques:

4. DISTANCE-BASED OUTLIER DETECTION

5. k parameter

6. RM Process

7. DENSITY-BASED OUTLIER DETECTION
Outliers occupy low-density areas
Normal data points occupy high-density areas

8. RM Process

9. LOCAL OUTLIER FACTOR
Detecting the outliers in varying density

10. RM Process