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Difference between revisions of "Anomaly Detection Tool Test Cases"

(Test Case 1)
 
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* No. of [http://sourceforge.net/p/niceproject/docs/Data Data] points: 1000
 
* No. of [http://sourceforge.net/p/niceproject/docs/Data 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.
 
* 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.
 +
[[File:2_clusters.jpg]]
  
 
== Test Case 2 ==
 
== Test Case 2 ==
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* No. of clusters: 2
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* No. of data points: 1000
 +
* Data points are not easily separable into 2 clusters as depicted in Fig.2.
 +
[[File:single_cluster.jpg]]
 +
 
== Test Case 3 LOF Test case ==
 
== 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.
 +
[[File:lof.jpg]]
 +
 
== Test Case 4 100 data points ==
 
== 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.
 +
[[File:2_clusters_less.jpg]]
 +
 
== 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.
 +
[[File:hairball_less.jpg]]
 +
 
== Test Case 6 LOF Test case: 100 data points ==
 
== 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.
 +
[[File:lof_less.jpg]]
 +
 +
== Related ==
 +
[https://wiki.eclipse.org/ICE_Developer_Documentation#Documentation​ Developer Documentation]

Latest revision as of 19:13, 26 February 2015

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.

2 clusters.jpg

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.

Single cluster.jpg

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.

Lof.jpg

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.

2 clusters less.jpg

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.

Hairball less.jpg

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.

Lof less.jpg

Related

Developer Documentation

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