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Difference between revisions of "Anomaly Detection Tool Test Cases"
(→Test Case 4 100 data points) |
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== Test Case 5 100 data points == | == 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. | ||
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== Test Case 6 LOF Test case: 100 data points == | == Test Case 6 LOF Test case: 100 data points == | ||
Revision as of 18:43, 26 February 2015
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.