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Anomaly Detection Tool Test Cases
Contents
Test Case 1
- No. of clusters: 2
- No. of Data points: 1000
- Data points are easily separable into 2 clusters as depicted in Fig.1. Kmeans clustering algorithm can easily identify two distinct clusters in this case.
Test Case 2
- No. of clusters: 2
- No. of data points: 1000
- Data points are not easily separable into 2 clusters as depicted in Fig.2.
Test Case 3 LOF Test case
- No. of clusters: 3
- No. of data points: 1000
- Data points are easily separable into 2 clusters, however anomalies are not easy to find) as depicted in Fig.3. The point marked with an arrow can be a potential anomaly, as we consider its local density.
Test Case 4 100 data points
- No. of clusters: 2
- No. of data points: 100
- Data points are easily separable into 2 clusters as depicted in Fig.4.
Test Case 5 100 data points
- No. of clusters: 2
- No. of data points: 100
- Data points are not easily separable into 2 clusters as depicted in Fig.5.
Test Case 6 LOF Test case: 100 data points
- No. of clusters: 3
- No. of data points: 100
- Data points are easily separable into 2 clusters, however anomalies are not easy to find) as depicted in Fig.6. The point marked with an arrow can be a potential anomaly, as we consider its local density.