DMBI Experiment no 4

Aim- Use WEKA to implement the following Clustering Algorithms-K- means, Agglomerative and Divisive.

kMeans
Number of iterations: 4
Within cluster sum of squared errors: 149.5177664581119
Missing values globally replaced with mean/mode
Cluster centroids:                                      Cluster#
Attribute                Full Data               0               1
                            (768)           (500)           (268)
==================================================================
preg                        3.8451           3.298          4.8657
plas                      120.8945          109.98        141.2575
pres                       69.1055          68.184         70.8246
skin                       20.5365          19.664         22.1642
insu                       79.7995          68.792        100.3358
mass                       31.9926         30.3042         35.1425
pedi                        0.4719          0.4297          0.5505
age                        33.2409           31.19         37.0672
class              tested_negative tested_negative tested_positive
Time taken to build model (full training data) : 0.02 seconds
=== Model and evaluation on training set ===
Clustered Instances
  1. 65%)
    268 ( 35%)


Hierarchical clustering
Cluster 0
((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((1.0:0.16243,1.0:0.16243):0.01512,1.0:0.17755):
Cluster 1
((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((0.0:0.12784,0.0:0.12784):0.02126,((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((
Time taken to build model (full training data) : 1.37 seconds
=== Model and evaluation on training set ===
Clustered Instances
0      268 ( 35%)
1      500 ( 65%)

DMBI Experiment no 4 DMBI Experiment no 4 Reviewed by akshay salve on 12:06 AM Rating: 5

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