Clustering_Resultas_Classeur_DI

=== Run information ===

Scheme:       weka.clusterers.SimpleKMeans -init 0 -max-candidates 100 -periodic-pruning 10000 -min-density 2.0 -t1 -1.25 -t2 -1.0 -N 4 -A "weka.core.EuclideanDistance -R first-last" -I 500 -num-slots 1 -S 10
Relation:     Classeur_DI
Instances:    10
Attributes:   4
              system
              UI
              SI
              SD
Test mode:    evaluate on training data


=== Clustering model (full training set) ===


kMeans
======

Number of iterations: 3
Within cluster sum of squared errors: 0.4099303404414516

Initial starting points (random):

Cluster 0: 4,0.55,0.42,0.38
Cluster 1: 7,0.45,0.5,0.38
Cluster 2: 3,0.5,0.33,0
Cluster 3: 6,0.45,0.5,0

Missing values globally replaced with mean/mode

Final cluster centroids:
                         Cluster#
Attribute    Full Data          0          1          2          3
                (10.0)      (3.0)      (2.0)      (3.0)      (2.0)
==================================================================
system             5.5     5.3333        9.5          2          7
UI                0.52     0.5167        0.7     0.4167        0.5
SI               0.436     0.4467      0.635     0.2767       0.46
SD               0.265       0.38       0.69          0      0.065




Time taken to build model (full training data) : 0 seconds


=== Model and evaluation on training set ===

Clustered Instances

0       3 ( 30%)
1       2 ( 20%)
2       2 ( 20%)
3       3 ( 30%)

Classer_DI

Cluster0   9,8,3
Cluster1   4,6
Cluster2   7,5
Cluster3   2,1,0

Cluster0   MidSem,FuhSen,LDIF
Cluster1   OD CleanStore,LSM
Cluster2   MOMIS,SWIS
Cluster3   Mastro,KRAFT,OBSERVER