Agglomerative clustering is the grouping of objects and people in clusters and groups. Most of the time, they are usually based on their similarities. A cluster is a group of items showing similar characteristics. A data set can be divided into smaller clusters depending on the uniqueness of the individual object or entity in the data ( Zhang & Xu, 2015). In this case, players can be into different groupings depending on the ERA as below.
|
Player |
ERA |
Salary |
|
1st Cluster |
||
|
1 |
2.53 |
17 |
|
2 |
2.54 |
4 |
|
2nd Cluster |
||
|
3 |
2.62 |
0.4 |
|
4 |
2.7 |
11 |
|
3rd Cluster |
||
|
5 |
2.78 |
10 |
|
6 |
2.85 |
6.5 |
|
7 |
2.9 |
8.3 |
|
8 |
2.96 |
7.3 |
|
4th Cluster |
||
|
9 |
3.09 |
12.1 |
|
10 |
3.09 |
0.5 |
Delegate your assignment to our experts and they will do the rest.
a)With the above clustering, the largest cluster is the third with the total number of four players.
The average ERA for the third cluster is equal to (2.78+2.85+2.9+2.96)/4
=11.49/4
=2.87
b) The average Salary third for cluster is equal to (4+6.5+8.3+73)/4
=26.1/4
=6.525
References
Zhou, S., Xu, Z., & Liu, F. (2016). Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE transactions on neural networks and learning systems , 28 (12), 3007-3017.
Zhang, X., & Xu, Z. (2015). Hesitant fuzzy agglomerative hierarchical clustering algorithms. International journal of systems Science , 46 (3), 562-576.
