Anomaly Detection Tool Test Cases

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.

Related

Developer Documentation