K-Means Clustering Example
1
K-Means Clustering ā Example
We recall from the previous lecture, that clustering allows for unsupervised learning.
That is, the machine / software will learn on its own, using the data (learning set), and
will classify the objects into a particular class ā for example, if our class (decision)
attribute is tumorType and its values are: malignant, benign, etc. - these will be the
classes. They will be represented by cluster1, cluster2, etc. However, the class
information is never provided to the algorithm. The class information can be used later
on, to evaluate how accurately the algorithm classified the objects.
(learning set)
Curvature
Texture
Blood
Consump
Tumor
Type
x1
0.8
1.2
A
Benign
x2
0.75
1.4
B
Benign
x3
0.23
0.4
D
Malignant
x4
.
.
0.23
0.5
D
Malignant
Curvature
Texture
Blood
Consump
Tumor
Type
x1
0.8
1.2
A
Benign
x2
0.75
1.4
B
Benign
x3
0.23
0.4
D
Malignant
x4
.
.
0.23
0.5
D
Malignant
Curvature
Texture
Blood
Consump
0.8
0.23
1.2
0.4
A
B
D
.x1
The way we do that, is by plotting the
objects from the database into space.
Each attribute is one dimension:
After all the objects are plotted, we
will calculate the distance between
them, and the ones that are close to
each other ā we will group them
together, i.e. place them in the same
cluster.
.
Curvature
Texture
Blood
Consump
0.8
0.23
1.2
0.4
A
B
D
.
.
.
.
.
.
.
Cluster 1
benign
Cluster 2
malignant
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