Cluster 1



[theory]



[Training set performance]

          Actual

       +        - 

     + 0        0        0 

Pred 

     - 1        67        68 



       1        67        68 



Accuracy = 0.985294117647059

[Training set summary] [[0,0,1,67]]

Cluster 0



[theory]



[Training set performance]

          Actual

       +        - 

     + 0        0        0 

Pred 

     - 1        67        68 



       1        67        68 



Accuracy = 0.985294117647059

[Training set summary] [[0,0,1,67]]

Cluster 3



[theory]



[Rule 1] [Pos cover = 32 Neg cover = 0]

target(A) :-

   ns45___cluster(A,'3').



[Training set performance]

          Actual

       +        - 

     + 32        0        32 

Pred 

     - 0        36        36 



       32        36        68 



Accuracy = 1.0

[Training set summary] [[32,0,0,36]]

Cluster 2



[theory]



[Training set performance]

          Actual

       +        - 

     + 0        0        0 

Pred 

     - 1        67        68 



       1        67        68 



Accuracy = 0.985294117647059

[Training set summary] [[0,0,1,67]]

Cluster 5



[theory]



[Rule 1] [Pos cover = 19 Neg cover = 0]

target(A) :-

   ns13___relatedToEvent(A,'http://www.eswc2006.org/sessions/#poster-session').



[Training set performance]

          Actual

       +        - 

     + 19        0        19 

Pred 

     - 0        49        49 



       19        49        68 



Accuracy = 1.0

[Training set summary] [[19,0,0,49]]

Cluster 4



[theory]



[Rule 1] [Pos cover = 14 Neg cover = 0]

target(A) :-

   ns13___relatedToEvent(A,'http://www.eswc2006.org/sessions/#demo-session').



[Training set performance]

          Actual

       +        - 

     + 14        0        14 

Pred 

     - 0        54        54 



       14        54        68 



Accuracy = 1.0

[Training set summary] [[14,0,0,54]]