Practical Applications of Data Mining

7.5 Clustering algorithms 301

in Table 7.14(b). The data of the insured is plotted in Figure 7.4 with the

number of claims against ages.

Following steps 1 and 2 of the hierarchical clustering, we can calculate the

distances between any pair of objects to obtain the result presented in Table

7.15. For example, the distance between C

1

and C

2

is D(C

1

, C

2

) = [(18 – 22)

2

+

(5 – 4)

2

]

1/2

= 4.12. From this table, it is easy to see that clusters C

6

and C

7

have

the smallest distance, so they should be merged first in step 3.

Merging C

6

with C

7

requires recalculation of the distances between the

new cluster C

67

and other clusters. The new matr